# FIESTA - Green-book Estimators

## Green-Book (GB) module overview

`FIESTA`‘s Green-Book (GB) module calculates population estimates and their sampling errors based on Bechtold and Patterson’s (2005), ’Green-Book’ for FIA’s nationally-consistent, systematic annual sample design, chapter 4 (Scott et al. 2005). FIA’s sample design is based on 2 phases: the first phase uses remotely-sensed data to stratify the land area to increase precision of estimates; while the 2nd phase obtains photo and ground observations and measurements for a suite of information across a hexagonal grid, each approximately 6000 acres in size. The associated estimators and variance estimators are used for area and tree attribute totals with the assumption of a simple random, stratified design and double sampling for stratification. Adjustment factors are calculated by estimation unit and strata to account for nonsampled (nonresponse) conditions.

Functions include non-ratio estimators for area and tree estimates by domain and ratio-of-means estimators for per-acre and per-tree estimates within domain. In addition, `FIESTA` adjusts for nonsampled conditions, supports post-stratification for reducing variance, and reports by estimation unit or a summed combination of estimation units. Output from the Green-Book module was tested and compared to output from FIA’s publicly-available online tool (EVALIDator) for state-level population estimates and associated sampling errors generated from the FIA Database (FIADB).

## Objective of tutorial

The Green-Book estimators can be used with FIA’s standard state-level population data (i.e, Evaluation) from the FIA database (FIADB) and also population data from a custom boundary. The population data includes a set of FIA plot data and summarized auxiliary information for post-stratification, including a table of area by estimation unit within the population, and a table of strata proportions by estimation unit. This tutorial steps through several examples using FIESTA’s Green Book module, for three different populations: (POP1) an FIA standard Evaluation, Wyoming 561301; (POP2) a custom boundary with one population, Bighorn National Forest; and (POP3) a custom boundary with sub-populations, Bighorn National Forest Districts. All examples can be used with any population, standard or custom.

## GB Examples

### GB Example Data

View GB Example Data

Example FIA plot data from FIADB

The examples use FIA plot data from FIA Evaluation 561301, including three inventory years of field measurements in the state of Wyoming, from FIADB_1.7.2.00, last updated June 20, 2018, downloaded on June 25, 2018, and stored as internal data objects in `FIESTA`.

Wyoming (WY), Inventory Years 2011-2013 (Evaluation 561301)

Data Frame | Description
--- | ---
WYplt | WY plot-level data
WYcond | WY condition-level data
WYtree | WY tree-level data

Example Auxiliary data

Auxiliary data for state-level estimates, including plot-level estimation unit and stratum assignments; area by estimation unit; and pixel counts by strata class and estimation unit, were downloaded from FIADB at the same time, from the same FIA Evaluation (i.e., 561301), and stored as internal data objects in `FIESTA`. Estimates using auxiliary data from FIADB can be compared with EVALIDator estimates, using the 2013 evaluation (https://apps.fs.usda.gov/fiadb-api/evalidator).

Auxiliary data for the custom boundaries are summarized from spatial layers stored as external objects in FIESTA, originating from the USDA Forest Service, Automated Lands Program (ALP; 2018) and from a 250m resolution, Moderate Resolution Imaging Spectroradiometer (MODIS), classified map, reclassified from 3 to 2 classes: 1:forest; 2:nonforest (Ruefenacht et al. 2008)

Wyoming (WY), Auxiliary data from FIADB (Evaluation 561301)

Data Frame | Description
--- | ---
WYpltassgn | WY plot-level data with strata and estimation unit assignments
WYunitarea | WY estimation unit look-up table with total acres by estimation unit (ESTUNIT)
WYstratalut | WY strata look-up table with pixel counts (P1POINTCNT) by strata and estimation unit

Wyoming (WY), Auxiliary data from other sources

External data | Description
--- | ---
WYbighorn_adminbnd.shp | Polygon shapefile of WY Bighorn National Forest Administrative boundary1
WYbighorn_districtbnd.shp | Polygon shapefile of WY Bighorn National Forest District boundaries2
WYbighorn_forest_nonforest_250m.tif | GeoTIFF raster of predicted forest/nonforest (1/0)3

1USDA Forest Service, Automated Lands Program (ALP). 2018. S_USA.AdministrativeForest (http://data.fs.usda.gov/geodata/edw). Description: An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area.

2USDA Forest Service, Automated Lands Program (ALP). 2018. S_USA.RangerDistrict (http://data.fs.usda.gov/geodata/edw). Description: A depiction of the boundary that encompareasses a Ranger District.

3Based on 250m resolution, Moderate Resolution Imaging Spectroradiometer (MODIS), classified map, reclassified from 3 to 2 classes: 1:forest; 2:nonforest. Projected in Albers Conical Equal Area, Datum NAD27 (Ruefenacht et al. 2008).

### Set up

First, you’ll need to load the `FIESTA` library:

    library(FIESTA)

Next, you’ll need to set up an “outfolder”. This is just a file path to a folder where you’d like `FIESTA` to send your data output. For this vignette, we have saved our outfolder file path as the `outfolder` object in a temporary directory. We also set a few default options preferred for this vignette.

    outfolder <- tempdir()

### Get auxiliary data for custom examples

Now, we need to get the auxiliary data for the custom boundaries. The FIESTA spGetStrata function is a spatial wrapper function to facilitate extraction and summary of user-defined spatial data used for post-stratification. The function uses the FIESTA spExtractPoly and spExtractRast functions to subset (i.e., clip) plots to the boundary and extract values from estimation unit (i.e., polygon) and strata values (i.e., raster) to plot center locations, respectively. Other internal spatial functions calculate stratum pixel counts and area by estimation unit. If a polygon strata layer is given, the FIESTA spPoly2Rast function converts the polygon layer to raster before calculating strata weights.

Our custom examples demonstrate how to get data for one area of interest, or population (e.g, Bighorn National Forest) and for one area of interest, with multiple estimation units, or subpopulations (e.g., Bighorn National Forest Districts).

Bighorn National Forest

View Getting Strata Data

    # File names for spatial layers, stored as external data objects in FIESTA.
    WYbhfn <- system.file("extdata", "sp_data/WYbighorn_adminbnd.shp", package="FIESTA")
    fornffn <- system.file("extdata", "sp_data/WYbighorn_forest_nonforest_250m.tif", package="FIESTA")

    # Get estimation unit and strata information for Bighorn National Forest.
    stratdat.bh <- spGetStrata(
          xyplt = WYplt,
          uniqueid = "CN",
          unit_layer = WYbhfn,
          strat_layer = fornffn,
          spMakeSpatial_opts = list(xvar = "LON_PUBLIC",
                                    yvar = "LAT_PUBLIC",
                                    xy.crs = 4269)
          )

output

    ## ================================================================================

    ## Get names of output list components
    names(stratdat.bh)

output

    ##  [1] "bnd"        "pltassgn"   "pltassgnid" "unitarea"   "unitvar"
    ##  [6] "areavar"    "areaunits"  "stratalut"  "strvar"     "getwt"
    ## [11] "strwtvar"

    ## Plot assignment of strata and estimation unit (ONEUNIT, STRATUMCD)
    head(stratdat.bh$pltassgn)

output

    ##               CN INVYR STATECD CYCLE UNITCD COUNTYCD  PLOT MEASYEAR RDDISTCD
    ## 1 40404876010690  2012      56     3      2        3 83143     2012        6
    ## 2 40404879010690  2011      56     3      2        3 80153     2011        6
    ## 3 40404886010690  2012      56     3      2        3 86397     2012       NA
    ## 4 40404893010690  2011      56     3      2        3 85901     2011        4
    ## 5 40404894010690  2013      56     3      2        3 90221     2013        6
    ## 6 40404899010690  2013      56     3      2        3 85144     2013       NA
    ##   NF_SAMPLING_STATUS_CD PLOT_STATUS_CD NF_PLOT_STATUS_CD NBRCND NBRCNDSAMP
    ## 1                     0              1                NA      2          2
    ## 2                     0              1                NA      1          1
    ## 3                     0              2                NA      1          1
    ## 4                     0              1                NA      1          1
    ## 5                     0              1                NA      1          1
    ## 6                     0              2                NA      1          1
    ##   NBRCNDFOR CCLIVEPLT        FORNONSAMP        PLOT_ID ONEUNIT STRATUMCD
    ## 1         2      67.5    Sampled-Forest ID560200383143       1         1
    ## 2         1      66.0    Sampled-Forest ID560200380153       1         1
    ## 3         0       0.0 Sampled-Nonforest ID560200386397       1         2
    ## 4         1      48.0    Sampled-Forest ID560200385901       1         2
    ## 5         1       2.0    Sampled-Forest ID560200390221       1         2
    ## 6         0       8.0 Sampled-Nonforest ID560200385144       1         2

    ## Area by estimation unit
    stratdat.bh$unitarea

output

    ##   ONEUNIT ACRES_GIS
    ## 1       1   1112388

    ## Pixel counts and strata weights (strwt) by strata and estimation unit
    stratdat.bh$stratalut

output

    ##   ONEUNIT STRATUMCD count     strwt
    ## 1       1         1 52280 0.7259397
    ## 2       1         2 19737 0.2740603

    ## Variable names
    stratdat.bh$unitvar        # Estimation unit variable

output

    ## [1] "ONEUNIT"

    stratdat.bh$strvar         # Strata variable

output

    ## [1] "STRATUMCD"

    stratdat.bh$areavar        # Area variable

output

    ## [1] "ACRES_GIS"

Bighorn National Forest Districts

View Getting Strata Data (Districts)

    # File names for external spatial data
    WYbhdistfn <- system.file("extdata", "sp_data/WYbighorn_districtbnd.shp", package="FIESTA")
    WYbhdist.att <- "DISTRICTNA"
    fornffn <- system.file("extdata", "sp_data/WYbighorn_forest_nonforest_250m.tif", package="FIESTA")

    # Get estimation unit and strata information for Bighorn National Forest Districts
    stratdat.bhdist <- spGetStrata(
          xyplt = WYplt,
          uniqueid = "CN",
          unit_layer=WYbhdistfn,
          unitvar=WYbhdist.att,
          strat_layer=fornffn,
          spMakeSpatial_opts = list(xvar = "LON_PUBLIC",
                                    yvar = "LAT_PUBLIC",
                                    xy.crs = 4269)
          )

output

    ## ================================================================================

    ## Get names of output list components
    names(stratdat.bhdist)

output

    ##  [1] "bnd"        "pltassgn"   "pltassgnid" "unitarea"   "unitvar"
    ##  [6] "areavar"    "areaunits"  "stratalut"  "strvar"     "getwt"
    ## [11] "strwtvar"

    ## Plot assignment of strata and estimation unit (DISTRICTNA, STRATUMCD)
    head(stratdat.bhdist$pltassgn)

output

    ##               CN INVYR STATECD CYCLE UNITCD COUNTYCD  PLOT MEASYEAR RDDISTCD
    ## 1 40404876010690  2012      56     3      2        3 83143     2012        6
    ## 2 40404879010690  2011      56     3      2        3 80153     2011        6
    ## 3 40404886010690  2012      56     3      2        3 86397     2012       NA
    ## 4 40404893010690  2011      56     3      2        3 85901     2011        4
    ## 5 40404894010690  2013      56     3      2        3 90221     2013        6
    ## 6 40404899010690  2013      56     3      2        3 85144     2013       NA
    ##   NF_SAMPLING_STATUS_CD PLOT_STATUS_CD NF_PLOT_STATUS_CD NBRCND NBRCNDSAMP
    ## 1                     0              1                NA      2          2
    ## 2                     0              1                NA      1          1
    ## 3                     0              2                NA      1          1
    ## 4                     0              1                NA      1          1
    ## 5                     0              1                NA      1          1
    ## 6                     0              2                NA      1          1
    ##   NBRCNDFOR CCLIVEPLT        FORNONSAMP        PLOT_ID
    ## 1         2      67.5    Sampled-Forest ID560200383143
    ## 2         1      66.0    Sampled-Forest ID560200380153
    ## 3         0       0.0 Sampled-Nonforest ID560200386397
    ## 4         1      48.0    Sampled-Forest ID560200385901
    ## 5         1       2.0    Sampled-Forest ID560200390221
    ## 6         0       8.0 Sampled-Nonforest ID560200385144
    ##                       DISTRICTNA STRATUMCD
    ## 1 Medicine Wheel Ranger District         1
    ## 2 Medicine Wheel Ranger District         1
    ## 3 Medicine Wheel Ranger District         2
    ## 4 Medicine Wheel Ranger District         2
    ## 5 Medicine Wheel Ranger District         2
    ## 6 Medicine Wheel Ranger District         2

    ## Area by estimation units (Districts)
    stratdat.bhdist$unitarea

output

    ##                       DISTRICTNA ACRES_GIS
    ## 1 Medicine Wheel Ranger District  364517.8
    ## 2   Powder River Ranger District  334330.4
    ## 3         Tongue Ranger District  413769.7

    ## Pixel counts and strata weights (strwt) by strata and estimation unit
    stratdat.bhdist$stratalut

output

    ##                       DISTRICTNA STRATUMCD count     strwt
    ## 1 Medicine Wheel Ranger District         1 14471 0.6127625
    ## 2 Medicine Wheel Ranger District         2  9145 0.3872375
    ## 3   Powder River Ranger District         1 15247 0.7043796
    ## 4   Powder River Ranger District         2  6399 0.2956204
    ## 5         Tongue Ranger District         1 22583 0.8436566
    ## 6         Tongue Ranger District         2  4185 0.1563434

    ## Variable names
    stratdat.bhdist$unitvar        # Estimation unit variable

output

    ## [1] "DISTRICTNA"

    stratdat.bhdist$strvar         # Strata variable

output

    ## [1] "STRATUMCD"

    stratdat.bhdist$areavar        # Area variable

output

    ## [1] "ACRES_GIS"

### GB Module

- modGBpop()
- modGBarea()
- modGBtree()
- modGBratio()

### modGBpop()

`FIESTA`’s population functions (`mod*pop`) check input data and perform population-level calculations, such as: summing number of sampled plots; adjusting for partial nonresponse; and standardizing auxiliary data. These functions are specific to each `FIESTA` module and are run prior to or within a module for any population of interest.

For `FIESTA`’s GB Module, the `modGBpop` function calculates and outputs: number of plots, adjustment factors, and an expansion factor by strata. The outputs are similar to data found in FIADB’s pop_stratum table. The output from `modGBpop` can be used for one or more estimates from `modGBarea`, `modGBtree`, or `modGBratio` functions.

#### POP1: FIADB POPULATION - Get population data for area and tree estimates for Wyoming, using post-stratification

View Example

In this example, we use the sample Wyoming data (2013 Evaluation) stored in `FIESTA` to generate population data for the GB module. We check this output with the FIADB pop_stratum table from FIA DataMart for 561301 Evalid, using the `FIESTA::DBqryCSV` function.

    GBpopdat <- modGBpop(
      popTabs = list(cond = FIESTA::WYcond,          # FIA plot/condition data
                     tree = FIESTA::WYtree,          # FIA tree data
                     seed = FIESTA::WYseed),         # FIA seedling data
      popTabIDs = list(cond = "PLT_CN"),             # unique ID of plot in cond
      pltassgn = FIESTA::WYpltassgn,  # plot assignments
      pltassgnid = "CN",              # unique ID of plot in pltassgn
      pjoinid = "PLT_CN",             # plot id to join to pltassgn
      unitarea = WYunitarea,          # area by estimation units
      unitvar = "ESTN_UNIT",          # name of estimation unit
      strata = TRUE,                  # if using post-stratification
      stratalut = WYstratalut,        # strata classes and pixels counts
      strata_opts = strata_options(getwt = TRUE)              # strata options
      )

To get the names of the list components associated with the output of our call of `modGBpop`, we run the following code:

    names(GBpopdat)

output

    ##  [1] "module"      "popType"     "condx"       "pltcondx"    "cuniqueid"
    ##  [6] "condid"      "ACI.filter"  "unitarea"    "areavar"     "areaunits"
    ## [11] "unitvar"     "unitvars"    "strata"      "stratalut"   "strvar"
    ## [16] "strwtvar"    "expcondtab"  "plotsampcnt" "condsampcnt" "states"
    ## [21] "invyrs"      "estvar.area" "adj"         "P2POINTCNT"  "treex"
    ## [26] "tuniqueid"   "adjtree"     "seedx"

From this list outputted by `GBpopdat` we can access many things. Some examples include the number of plots by plot status that can be accessed with the `plotsampcnt` item, the number of conditions by condition status with `condsampcnt`, the strata-level population data, including number of plots and adjustment factors with `stratalut`, and the adjustment factors added to the condition-level, tree-level, and seedling data with `condx`, `treex`, and `seedx`, respectfully. These objects can be seen below:

    ## Look at output from GBpopdat
    GBpopdat$plotsampcnt    # Number of plots by plot status

output

    ##                                                            PLOT_STATUS_NM
    ## 1 Sampled - at least one accessible forest land condition present on plot
    ## 2           Sampled - no accessible forest land condition present on plot
    ##   PLOT_STATUS_CD NBRPLOT
    ## 1              1     556
    ## 2              2    2491

    GBpopdat$condsampcnt    # Number of conditions by condition status

output

    ##    COND_STATUS_NM COND_STATUS_CD NBRCOND
    ## 1     Forest land              1     590
    ## 2  Nonforest land              2    2590
    ## 3 Noncensus water              3      10
    ## 4    Census water              4      20
    ## 5      Nonsampled              5      14

    # Strata-level population data, including number of plots and adjustment factors
    GBpopdat$stratalut

output

    ##     ESTN_UNIT STRATUMCD P1POINTCNT P2POINTCNT n.total n.strata      strwt
    ##  1:         1         1      30603         17     133       17 0.17138393
    ##  2:         1         2     147961        116     133      116 0.82861607
    ##  3:         3         1      15896         12      98       12 0.12145384
    ##  4:         3         2     114985         86      98       86 0.87854616
    ##  5:         5         2     198981        152     152      152 1.00000000
    ##  6:         7         1      50473         35     245       35 0.15293736
    ##  7:         7         2     279551        210     245      210 0.84706264
    ##  8:         9         1      13946         16     133       16 0.07891401
    ##  9:         9         2     162778        117     133      117 0.92108599
    ## 10:        11         1      34965         28      85       28 0.29395692
    ## 11:        11         2      83981         57      85       57 0.70604308
    ## 12:        13         1      60592         48     290       48 0.15780564
    ## 13:        13         2     323374        242     290      242 0.84219436
    ## 14:        15         2      92483         70      70       70 1.00000000
    ## 15:        17         2      83149         58      58       58 1.00000000
    ## 16:        19         1      24652         18     128       18 0.14250699
    ## 17:        19         2     148336        110     128      110 0.85749301
    ## 18:        21         2     111389         86      86       86 1.00000000
    ## 19:        23         1      49359         35     132       35 0.29129978
    ## 20:        23         2     120085         97     132       97 0.70870022
    ## 21:        25         2     222755        175     175      175 1.00000000
    ## 22:        27         2     108902         79      79       79 1.00000000
    ## 23:        29         1     140049        100     216      100 0.48499131
    ## 24:        29         2     148717        116     216      116 0.51500869
    ## 25:        31         2      87474         64      64       64 1.00000000
    ## 26:        33         1      24037         18      82       18 0.22951399
    ## 27:        33         2      80693         64      82       64 0.77048601
    ## 28:        35         1      55527         44     158       44 0.27151107
    ## 29:        35         2     148984        114     158      114 0.72848893
    ## 30:        37         2     434729        339     339      339 1.00000000
    ## 31:        39         1     128994         98     125       98 0.73730659
    ## 32:        39         2      45959         27     125       27 0.26269341
    ## 33:        41         2      86508         63      63       63 1.00000000
    ## 34:        43         2      92938         63      63       63 1.00000000
    ## 35:        45         2      99461         73      73       73 1.00000000
    ##     ESTN_UNIT STRATUMCD P1POINTCNT P2POINTCNT n.total n.strata      strwt
    ##       ACRES    expfac    EXPNS ADJ_FACTOR_COND ADJ_FACTOR_SUBP ADJ_FACTOR_MACR
    ##  1: 2757613 162212.53 27800.62        1.014925        1.014925               0
    ##  2: 2757613  23772.53 19698.30        1.000000        1.000000               0
    ##  3: 2021729 168477.42 20462.23        1.000000        1.000000               0
    ##  4: 2021729  23508.48 20653.28        1.008798        1.008798               0
    ##  5: 3072988  20217.03 20217.03        1.000000        1.000000               0
    ##  6: 5096959 145627.40 22271.87        1.000000        1.000000               0
    ##  7: 5096959  24271.23 20559.25        1.000000        1.000000               0
    ##  8: 2729653 170603.31 13462.99        1.015873        1.015873               0
    ##  9: 2729653  23330.37 21489.27        1.000000        1.000000               0
    ## 10: 1837124  65611.57 19286.98        1.000000        1.000000               0
    ## 11: 1837124  32230.25 22755.94        1.000000        1.000000               0
    ## 12: 5930088 123543.50 19495.86        1.005236        1.005236               0
    ## 13: 5930088  24504.50 20637.55        1.000000        1.000000               0
    ## 14: 1428579  20408.27 20408.27        1.000000        1.000000               0
    ## 15: 1283969  22137.40 22137.40        1.000000        1.000000               0
    ## 16: 2671802 148433.44 21152.80        1.014085        1.014085               0
    ## 17: 2671802  24289.11 20827.74        1.000000        1.000000               0
    ## 18: 1720074  20000.86 20000.86        1.000000        1.000000               0
    ## 19: 2616954  74770.11 21780.52        1.007194        1.007194               0
    ## 20: 2616954  26978.91 19119.96        1.000000        1.000000               0
    ## 21: 3440445  19659.69 19659.69        1.000000        1.000000               0
    ## 22: 1681849  21289.23 21289.23        1.000000        1.000000               0
    ## 23: 4459826  44598.26 21629.77        1.007557        1.007557               0
    ## 24: 4459826  38446.78 19800.42        1.002160        1.002160               0
    ## 25: 1350969  21108.89 21108.89        1.000000        1.000000               0
    ## 26: 1617318  89851.00 20622.06        1.014085        1.014085               0
    ## 27: 1617318  25270.59 19470.64        1.000000        1.000000               0
    ## 28: 3158807  71791.07 19492.07        1.000000        1.000000               0
    ## 29: 3158807  27708.83 20185.58        1.000000        1.000000               0
    ## 30: 6714319  19806.25 19806.25        1.000000        1.000000               0
    ## 31: 2701941  27570.83 20328.15        1.007712        1.007712               0
    ## 32: 2701941 100071.89 26288.23        1.000000        1.000000               0
    ## 33: 1336034  21206.89 21206.89        1.000000        1.000000               0
    ## 34: 1435352  22783.37 22783.37        1.000000        1.000000               0
    ## 35: 1536038  21041.62 21041.62        1.006897        1.006897               0
    ##       ACRES    expfac    EXPNS ADJ_FACTOR_COND ADJ_FACTOR_SUBP ADJ_FACTOR_MACR
    ##     ADJ_FACTOR_MICR
    ##  1:        1.014925
    ##  2:        1.000000
    ##  3:        1.000000
    ##  4:        1.008798
    ##  5:        1.000000
    ##  6:        1.000000
    ##  7:        1.000000
    ##  8:        1.015873
    ##  9:        1.000000
    ## 10:        1.000000
    ## 11:        1.000000
    ## 12:        1.005236
    ## 13:        1.000000
    ## 14:        1.000000
    ## 15:        1.000000
    ## 16:        1.014085
    ## 17:        1.000000
    ## 18:        1.000000
    ## 19:        1.007194
    ## 20:        1.000000
    ## 21:        1.000000
    ## 22:        1.000000
    ## 23:        1.007557
    ## 24:        1.002160
    ## 25:        1.000000
    ## 26:        1.014085
    ## 27:        1.000000
    ## 28:        1.000000
    ## 29:        1.000000
    ## 30:        1.000000
    ## 31:        1.007712
    ## 32:        1.000000
    ## 33:        1.000000
    ## 34:        1.000000
    ## 35:        1.006897
    ##     ADJ_FACTOR_MICR

    ## Adjustment factors added to condition-level data
    GBpopdat$condx

output

    ##               PLT_CN CONDID CONDPROP_UNADJ SUBPPROP_UNADJ MICRPROP_UNADJ
    ##    1: 40404728010690      1              1              1              1
    ##    2: 40404729010690      1              1              1              1
    ##    3: 40404730010690      1              1              1              1
    ##    4: 40404731010690      1              1              1              1
    ##    5: 40404733010690      1              1              1              1
    ##   ---
    ## 3206: 40407866010690      1              1              1              1
    ## 3207: 40407867010690      1              1              1              1
    ## 3208: 40407868010690      1              1              1              1
    ## 3209: 40407869010690      1              1              1              1
    ## 3210: 46792188020004      1              1              1              1
    ##       MACRPROP_UNADJ ESTN_UNIT STRATUMCD ADJ_FACTOR_COND ADJ_FACTOR_SUBP
    ##    1:             NA         1         2        1.000000        1.000000
    ##    2:             NA         1         2        1.000000        1.000000
    ##    3:             NA         1         1        1.014925        1.014925
    ##    4:             NA         1         2        1.000000        1.000000
    ##    5:             NA         1         2        1.000000        1.000000
    ##   ---
    ## 3206:             NA        45         2        1.006897        1.006897
    ## 3207:             NA        45         2        1.006897        1.006897
    ## 3208:             NA        45         2        1.006897        1.006897
    ## 3209:             NA        45         2        1.006897        1.006897
    ## 3210:             NA        29         1        1.007557        1.007557
    ##       ADJ_FACTOR_MACR ADJ_FACTOR_MICR CONDPROP_ADJ
    ##    1:               0        1.000000     1.000000
    ##    2:               0        1.000000     1.000000
    ##    3:               0        1.014925     1.014925
    ##    4:               0        1.000000     1.000000
    ##    5:               0        1.000000     1.000000
    ##   ---
    ## 3206:               0        1.006897     1.006897
    ## 3207:               0        1.006897     1.006897
    ## 3208:               0        1.006897     1.006897
    ## 3209:               0        1.006897     1.006897
    ## 3210:               0        1.007557     1.007557

    ## Adjustment factors added to tree data
    GBpopdat$treex

output

    ##                PLT_CN CONDID SUBP TREE STATUSCD SPCD SPGRPCD  DIA HT TREECLCD
    ##     1: 40404729010690      1    1    2        1   66      23 10.8 14        3
    ##     2: 40404729010690      1    4    3        1   66      23 11.6 19        3
    ##     3: 40404729010690      1    1    1        2  113      24  7.7 18        3
    ##     4: 40404729010690      1    1    3        2  113      24  5.2 23        3
    ##     5: 40404729010690      1    1    4        1  113      24  5.2 18        2
    ##    ---
    ## 18376: 46792188020004      1    4   13        1  202      10  7.0 26        2
    ## 18377: 46792188020004      1    4   14        2  202      10 11.4 53        3
    ## 18378: 46792188020004      1    4   15        2  202      10 10.6 46        3
    ## 18379: 46792188020004      1    4   16        2  202      10  6.3 41        3
    ## 18380: 46792188020004      1    4   17        1  202      10  1.9  8        3
    ##        AGENTCD STANDING_DEAD_CD  VOLCFNET  VOLCFGRS VOLBFNET TPA_UNADJ
    ##     1:      NA               NA  3.411463  3.411463       NA  6.018046
    ##     2:      NA               NA  5.623683  5.797611       NA  6.018046
    ##     3:      10                1  1.067166  1.940302       NA  6.018046
    ##     4:      10                1  0.381894  0.694352       NA  6.018046
    ##     5:      NA               NA  0.316063  0.316063       NA  6.018046
    ##    ---
    ## 18376:      NA               NA  2.125580  2.125580       NA  6.018046
    ## 18377:      NA                1 15.149782 15.149782       NA  6.018046
    ## 18378:      NA                1 11.160959 11.160959       NA  6.018046
    ## 18379:      NA                1  2.945213  2.945213       NA  6.018046
    ## 18380:      NA               NA        NA        NA       NA 74.965282
    ##         DRYBIO_AG  CARBON_AG         BA TPROP_BASIS  tadjfac
    ##     1:  94.516629  47.258315 0.63615456        SUBP 1.000000
    ##     2: 155.807512  77.903756 0.73389024        SUBP 1.000000
    ##     3:  30.761698  15.380849 0.32336766        SUBP 1.000000
    ##     4:  11.305782   5.652891 0.14747616        SUBP 1.000000
    ##     5:  11.210763   5.605382 0.14747616        SUBP 1.000000
    ##    ---
    ## 18376:  88.388202  44.194101 0.26724600        SUBP 1.007557
    ## 18377: 305.250275 152.625138 0.70880184        SUBP 1.007557
    ## 18378: 225.732139 112.866070 0.61281144        SUBP 1.007557
    ## 18379:  61.586094  30.793047 0.21646926        SUBP 1.007557
    ## 18380:   5.065642   2.532821 0.01968894        MICR 1.007557

    ## Adjustment factors added to seedling data
    GBpopdat$seedx

output

    ##               PLT_CN SUBP CONDID SPCD SPGRPCD  TPA_UNADJ TREECOUNT
    ##    1: 40404729010690    2      1  113      24   74.96528         1
    ##    2: 40404730010690    2      1  202      10  224.89585         3
    ##    3: 40404738010690    2      1  746      44 2323.92376        31
    ##    4: 40404738010690    4      1   19      12   74.96528         1
    ##    5: 40404738010690    4      1  113      24   74.96528         1
    ##   ---
    ## 1603: 40407815010690    1      1  313      47  224.89585         3
    ## 1604: 40407831010690    1      1  122      11 4497.91695        60
    ## 1605: 46792188020004    2      1  202      10  374.82641         5
    ## 1606: 46792188020004    3      1  202      10  299.86113         4
    ## 1607: 46792188020004    4      1  202      10  224.89585         3
    ##       TREECOUNT_CALC SEEDCNT6 SEEDSUBP6  tadjfac
    ##    1:              1        1         0 1.000000
    ##    2:              3        3         0 1.014925
    ##    3:             31        6         1 1.014925
    ##    4:              1        1         0 1.014925
    ##    5:              1        1         0 1.014925
    ##   ---
    ## 1603:              3        3         0 1.006897
    ## 1604:             60        6         1 1.006897
    ## 1605:              5        5         0 1.007557
    ## 1606:              4        4         0 1.007557
    ## 1607:              3        3         0 1.007557

One may also want to compare `FIESTA` output with FIADB pop_stratum table for WY in the 2013 evaluation to check for consistency. The can be done as follows:

    qry <- "select estn_unit, stratumcd, p1pointcnt, p2pointcnt, expns,
              adj_factor_macr, adj_factor_subp, adj_factor_micr from pop_stratum
            where evalid = 561301 order by estn_unit, stratumcd"
    pop_stratum <- tryCatch(
            DBqryCSV(
                      qry,
                      states="Wyoming",
                      sqltables="pop_stratum"
                      ),
                error=function(e) {
                return(NULL) })

    if (!is.null(pop_stratum)) {
      head(pop_stratum)
    }
    head(GBpopdat$stratalut)

#### POP2: CUSTOM POPULATION - Get population data for area and tree estimates for the Bighorn National Forest, using post-stratification

View Example

In this example, we use the sample WY plot data (2013 Evaluation) in FIESTA and output from `spGetStrata` to generate population data for the Bighorn National Forest. Here, we have only one estimation unit within the population of interest (i.e., Bighorn National Forest), therefore strata and pixel counts are summarized to the population.

If the `FIESTA::spGetStrata` function is used to obtain stratification data, the output list object can be input directly into `modGBpop` through the `GBstratdat` parameter. If other methods are used, the data are input through individual parameters.

    ## Bighorn National Forest

    ## Using output list from spGetStrata()
    GBpopdat.bh <- modGBpop(
          popTabs=list(plt=WYplt, cond=WYcond, tree=WYtree, seed=WYseed),
          stratdat=stratdat.bh)

    ## Get names of output list components
    names(GBpopdat.bh)

output

    ##  [1] "module"      "popType"     "condx"       "pltcondx"    "cuniqueid"
    ##  [6] "condid"      "ACI.filter"  "unitarea"    "areavar"     "areaunits"
    ## [11] "unitvar"     "unitvars"    "strata"      "stratalut"   "strvar"
    ## [16] "strwtvar"    "expcondtab"  "plotsampcnt" "condsampcnt" "states"
    ## [21] "invyrs"      "estvar.area" "adj"         "P2POINTCNT"  "treex"
    ## [26] "tuniqueid"   "adjtree"     "seedx"

    ## Using output as individual parameter inputs
    GBpopdat.bh <- modGBpop(
          popTabs=list(plt=WYplt, cond=WYcond, tree=WYtree, seed=WYseed),
          popTabIDs=list(plt="CN"),
          pltassgn=stratdat.bh$pltassgn,
          pltassgnid="CN",
          unitvar=stratdat.bh$unitvar,
          unitarea=stratdat.bh$unitarea,
          areavar=stratdat.bh$areavar,
          strata=TRUE,
          stratalut=stratdat.bh$stratalut,
          strvar=stratdat.bh$strvar
          )

    ## Get names of output list components
    names(GBpopdat.bh)

output

    ##  [1] "module"      "popType"     "condx"       "pltcondx"    "cuniqueid"
    ##  [6] "condid"      "ACI.filter"  "unitarea"    "areavar"     "areaunits"
    ## [11] "unitvar"     "unitvars"    "strata"      "stratalut"   "strvar"
    ## [16] "strwtvar"    "expcondtab"  "plotsampcnt" "condsampcnt" "states"
    ## [21] "invyrs"      "estvar.area" "adj"         "P2POINTCNT"  "treex"
    ## [26] "tuniqueid"   "adjtree"     "seedx"

    ## Condition information with adjusted condition proportions for area
    head(GBpopdat.bh$condx)

output

    ##            PLT_CN CONDID CONDPROP_UNADJ SUBPPROP_UNADJ MICRPROP_UNADJ
    ## 1: 40404876010690      1           0.75           0.75           0.75
    ## 2: 40404876010690      2           0.25           0.25           0.25
    ## 3: 40404879010690      1           1.00           1.00           1.00
    ## 4: 40404886010690      1           1.00           1.00           1.00
    ## 5: 40404893010690      1           1.00           1.00           1.00
    ## 6: 40404894010690      1           1.00           1.00           1.00
    ##    MACRPROP_UNADJ ONEUNIT STRATUMCD ADJ_FACTOR_COND ADJ_FACTOR_SUBP
    ## 1:             NA       1         1        1.006135        1.006135
    ## 2:             NA       1         1        1.006135        1.006135
    ## 3:             NA       1         1        1.006135        1.006135
    ## 4:             NA       1         2        1.000000        1.000000
    ## 5:             NA       1         2        1.000000        1.000000
    ## 6:             NA       1         2        1.000000        1.000000
    ##    ADJ_FACTOR_MACR ADJ_FACTOR_MICR CONDPROP_ADJ
    ## 1:               0        1.006135    0.7546012
    ## 2:               0        1.006135    0.2515337
    ## 3:               0        1.006135    1.0061350
    ## 4:               0        1.000000    1.0000000
    ## 5:               0        1.000000    1.0000000
    ## 6:               0        1.000000    1.0000000

    ## Tree information with tree-level adjustment factors
    head(GBpopdat.bh$treex)

output

    ##            PLT_CN CONDID SUBP TREE STATUSCD SPCD SPGRPCD  DIA HT TREECLCD
    ## 1: 40404876010690      1    1    9        1   19      12 12.1 64        2
    ## 2: 40404876010690      1    1   15        1   19      12  1.5 11        2
    ## 3: 40404876010690      1    2   18        2   19      12 12.0 57        3
    ## 4: 40404876010690      1    2   22        2   19      12  5.5 53        3
    ## 5: 40404876010690      1    2   26        1   19      12  1.8 12        2
    ## 6: 40404876010690      1    4    3        1   19      12  6.9 46        2
    ##    AGENTCD STANDING_DEAD_CD  VOLCFNET  VOLCFGRS VOLBFNET TPA_UNADJ  DRYBIO_AG
    ## 1:      NA               NA 20.908857 20.908857 104.7298  6.018046 581.902600
    ## 2:      NA               NA        NA        NA       NA 74.965282   2.410252
    ## 3:      70                1  6.374361 18.212460       NA  6.018046 167.502609
    ## 4:      NA                1  2.889440  2.889440       NA  6.018046  67.437885
    ## 5:      NA               NA        NA        NA       NA 74.965282   3.925288
    ## 6:      NA               NA  4.250839  4.250839       NA  6.018046 122.432583
    ##     CARBON_AG         BA TPROP_BASIS  tadjfac
    ## 1: 290.951300 0.79852014        SUBP 1.006135
    ## 2:   1.205126 0.01227150        MICR 1.006135
    ## 3:  83.751305 0.78537600        SUBP 1.006135
    ## 4:  33.718943 0.16498350        SUBP 1.006135
    ## 5:   1.962644 0.01767096        MICR 1.006135
    ## 6:  61.216292 0.25966494        SUBP 1.006135

    ## Seedling information with adjustment factors
    head(GBpopdat.bh$seedx)

output

    ##            PLT_CN SUBP CONDID SPCD SPGRPCD TPA_UNADJ TREECOUNT TREECOUNT_CALC
    ## 1: 40404876010690    1      1   19      12 299.86113         4              4
    ## 2: 40404876010690    1      1  108      21 374.82641         5              5
    ## 3: 40404876010690    2      1   19      12 374.82641         5              5
    ## 4: 40404876010690    2      1  108      21  74.96528         1              1
    ## 5: 40404876010690    3      2   19      12 224.89585         3              3
    ## 6: 40404876010690    3      2   93      18 149.93057         2              2
    ##    SEEDCNT6 SEEDSUBP6  tadjfac
    ## 1:        4         0 1.006135
    ## 2:        5         0 1.006135
    ## 3:        5         0 1.006135
    ## 4:        1         0 1.006135
    ## 5:        3         0 1.006135
    ## 6:        2         0 1.006135

    ## Strata-level information, including number of plots by strata and strata-level adjustment factors
    GBpopdat.bh$stratalut

output

    ##    ONEUNIT STRATUMCD     strwt P2POINTCNT n.total n.strata ACRES_GIS   expfac
    ## 1:       1         1 0.7259397         41      56       41   1112388 27131.42
    ## 2:       1         2 0.2740603         15      56       15   1112388 74159.21
    ##       EXPNS ADJ_FACTOR_COND ADJ_FACTOR_SUBP ADJ_FACTOR_MACR ADJ_FACTOR_MICR
    ## 1: 19695.77        1.006135        1.006135               0        1.006135
    ## 2: 20324.09        1.000000        1.000000               0        1.000000

#### POP3: CUSTOM SUB-POPULATIONS - Get sub-population data for area and tree estimates for the Bighorn National Forest Districts, using post-stratification

View Example

In this example, we use the sample Wyoming plot data (2013 Evaluation) stored in FIESTA and output from `spGetStrata` to generate sub-population data for Bighorn National Forest Districts. Here, we have more than one estimation unit (i.e., sub-population) within the population of interest (i.e., Bighorn National Forest Districts), therefore strata and pixel counts are summarized by each District within the population.

If the `FIESTA::spGetStrata` function is used to obtain stratification data, the output list object can be input directly into `modGBpop` through the `GBstratdat` parameter. If other methods are used, the data are input through individual parameters.

    ## Bighorn National Forest District

    ## Using output list from spGetStrata()
    GBpopdat.bhdist <- modGBpop(
          popTabs=list(plt=WYplt, cond=WYcond, tree=WYtree, seed=WYseed),
          stratdat=stratdat.bhdist)

    ## Get names of output list components
    names(GBpopdat.bhdist)

output

    ##  [1] "module"      "popType"     "condx"       "pltcondx"    "cuniqueid"
    ##  [6] "condid"      "ACI.filter"  "unitarea"    "areavar"     "areaunits"
    ## [11] "unitvar"     "unitvars"    "strata"      "stratalut"   "strvar"
    ## [16] "strwtvar"    "expcondtab"  "plotsampcnt" "condsampcnt" "states"
    ## [21] "invyrs"      "estvar.area" "adj"         "P2POINTCNT"  "treex"
    ## [26] "tuniqueid"   "adjtree"     "seedx"

    GBpopdat.bhdist <- modGBpop(
          popTabs=list(plt=WYplt, cond=WYcond, tree=WYtree, seed=WYseed),
          pltassgn=stratdat.bhdist$pltassgn,
          pltassgnid="CN",
          unitvar=stratdat.bhdist$unitvar,
          unitarea=stratdat.bhdist$unitarea,
          areavar=stratdat.bhdist$areavar,
          strata=TRUE,
          stratalut=stratdat.bhdist$stratalut,
          strvar=stratdat.bhdist$strvar
          )

    ## Get names of output list components
    names(GBpopdat.bhdist)

output

    ##  [1] "module"      "popType"     "condx"       "pltcondx"    "cuniqueid"
    ##  [6] "condid"      "ACI.filter"  "unitarea"    "areavar"     "areaunits"
    ## [11] "unitvar"     "unitvars"    "strata"      "stratalut"   "strvar"
    ## [16] "strwtvar"    "expcondtab"  "plotsampcnt" "condsampcnt" "states"
    ## [21] "invyrs"      "estvar.area" "adj"         "P2POINTCNT"  "treex"
    ## [26] "tuniqueid"   "adjtree"     "seedx"

    ## Condition information with adjusted condition proportions for area
    head(GBpopdat.bhdist$condx)

output

    ##            PLT_CN CONDID CONDPROP_UNADJ SUBPPROP_UNADJ MICRPROP_UNADJ
    ## 1: 40404876010690      1           0.75           0.75           0.75
    ## 2: 40404876010690      2           0.25           0.25           0.25
    ## 3: 40404879010690      1           1.00           1.00           1.00
    ## 4: 40404886010690      1           1.00           1.00           1.00
    ## 5: 40404893010690      1           1.00           1.00           1.00
    ## 6: 40404894010690      1           1.00           1.00           1.00
    ##    MACRPROP_UNADJ                     DISTRICTNA STRATUMCD ADJ_FACTOR_COND
    ## 1:             NA Medicine Wheel Ranger District         1        1.028571
    ## 2:             NA Medicine Wheel Ranger District         1        1.028571
    ## 3:             NA Medicine Wheel Ranger District         1        1.028571
    ## 4:             NA Medicine Wheel Ranger District         2        1.000000
    ## 5:             NA Medicine Wheel Ranger District         2        1.000000
    ## 6:             NA Medicine Wheel Ranger District         2        1.000000
    ##    ADJ_FACTOR_SUBP ADJ_FACTOR_MACR ADJ_FACTOR_MICR CONDPROP_ADJ
    ## 1:        1.028571               0        1.028571    0.7714286
    ## 2:        1.028571               0        1.028571    0.2571429
    ## 3:        1.028571               0        1.028571    1.0285714
    ## 4:        1.000000               0        1.000000    1.0000000
    ## 5:        1.000000               0        1.000000    1.0000000
    ## 6:        1.000000               0        1.000000    1.0000000

    ## Tree information with tree-level adjustment factors
    head(GBpopdat.bhdist$treex)

output

    ##            PLT_CN CONDID SUBP TREE STATUSCD SPCD SPGRPCD  DIA HT TREECLCD
    ## 1: 40404876010690      1    1    9        1   19      12 12.1 64        2
    ## 2: 40404876010690      1    1   15        1   19      12  1.5 11        2
    ## 3: 40404876010690      1    2   18        2   19      12 12.0 57        3
    ## 4: 40404876010690      1    2   22        2   19      12  5.5 53        3
    ## 5: 40404876010690      1    2   26        1   19      12  1.8 12        2
    ## 6: 40404876010690      1    4    3        1   19      12  6.9 46        2
    ##    AGENTCD STANDING_DEAD_CD  VOLCFNET  VOLCFGRS VOLBFNET TPA_UNADJ  DRYBIO_AG
    ## 1:      NA               NA 20.908857 20.908857 104.7298  6.018046 581.902600
    ## 2:      NA               NA        NA        NA       NA 74.965282   2.410252
    ## 3:      70                1  6.374361 18.212460       NA  6.018046 167.502609
    ## 4:      NA                1  2.889440  2.889440       NA  6.018046  67.437885
    ## 5:      NA               NA        NA        NA       NA 74.965282   3.925288
    ## 6:      NA               NA  4.250839  4.250839       NA  6.018046 122.432583
    ##     CARBON_AG         BA TPROP_BASIS  tadjfac
    ## 1: 290.951300 0.79852014        SUBP 1.028571
    ## 2:   1.205126 0.01227150        MICR 1.028571
    ## 3:  83.751305 0.78537600        SUBP 1.028571
    ## 4:  33.718943 0.16498350        SUBP 1.028571
    ## 5:   1.962644 0.01767096        MICR 1.028571
    ## 6:  61.216292 0.25966494        SUBP 1.028571

    ## Seedling information with adjustment factors
    head(GBpopdat.bhdist$seedx)

output

    ##            PLT_CN SUBP CONDID SPCD SPGRPCD TPA_UNADJ TREECOUNT TREECOUNT_CALC
    ## 1: 40404876010690    1      1   19      12 299.86113         4              4
    ## 2: 40404876010690    1      1  108      21 374.82641         5              5
    ## 3: 40404876010690    2      1   19      12 374.82641         5              5
    ## 4: 40404876010690    2      1  108      21  74.96528         1              1
    ## 5: 40404876010690    3      2   19      12 224.89585         3              3
    ## 6: 40404876010690    3      2   93      18 149.93057         2              2
    ##    SEEDCNT6 SEEDSUBP6  tadjfac
    ## 1:        4         0 1.028571
    ## 2:        5         0 1.028571
    ## 3:        5         0 1.028571
    ## 4:        1         0 1.028571
    ## 5:        3         0 1.028571
    ## 6:        2         0 1.028571

    ## Strata-level information, including number of plots by strata and strata-level adjustment factors
    GBpopdat.bhdist$stratalut

output

    ##                        DISTRICTNA STRATUMCD     strwt P2POINTCNT n.total
    ## 1: Medicine Wheel Ranger District         1 0.6127625          9      16
    ## 2: Medicine Wheel Ranger District         2 0.3872375          7      16
    ## 3:   Powder River Ranger District         1 0.7043796         13      19
    ## 4:   Powder River Ranger District         2 0.2956204          6      19
    ## 5:         Tongue Ranger District         1 0.8436566         19      21
    ## 6:         Tongue Ranger District         2 0.1563434          2      21
    ##    n.strata ACRES_GIS    expfac    EXPNS ADJ_FACTOR_COND ADJ_FACTOR_SUBP
    ## 1:        9  364517.8  40501.98 24818.09        1.028571        1.028571
    ## 2:        7  364517.8  52073.97 20164.99        1.000000        1.000000
    ## 3:       13  334330.4  25717.72 18115.04        1.000000        1.000000
    ## 4:        6  334330.4  55721.74 16472.48        1.000000        1.000000
    ## 5:       19  413769.7  21777.36 18372.61        1.000000        1.000000
    ## 6:        2  413769.7 206884.87 32345.08        1.000000        1.000000
    ##    ADJ_FACTOR_MACR ADJ_FACTOR_MICR
    ## 1:               0        1.028571
    ## 2:               0        1.000000
    ## 3:               0        1.000000
    ## 4:               0        1.000000
    ## 5:               0        1.000000
    ## 6:               0        1.000000

#### POP4: FIADB POPULATION - Get population data for area and tree estimates for Rhode Island, using post-stratification, with data stored in a SQLite database

View Example

In this example, we use the sample Rhode Island data (441901 Evaluation) stored in a SQLite database as external data in `FIESTA`. Data were extracted from the FIA database on June 6, 2022. All output can be compared with output from other FIA tools.

First, let’s look at the SQLite database. Use the DBI package explore the contents.

    SQLitefn <- system.file("extdata", "FIA_data/RIdat_eval2019.db", package="FIESTA")

    conn <- DBI::dbConnect(RSQLite::SQLite(), SQLitefn)
    DBI::dbListTables(conn)
    DBI::dbDisconnect(conn)

    GBpopdat.RI <- modGBpop(popTabs = list(plt="plot", cond="cond", tree="tree", seed="seed"),
                      dsn = SQLitefn,
                      pltassgn = "pop_plot_stratum_assgn",
                      stratalut = "pop_stratum",
                      unitarea = "pop_estn_unit",
                      unitvar = "ESTN_UNIT",
                      areavar = "AREA_USED",
                      strata_opts = list(getwt=TRUE, getwtvar="P1POINTCNT")
                      )
    names(GBpopdat.RI)

    # Strata-level population data, including number of plots and adjustment factors
    GBpopdat.RI$stratalut

### modGBarea()

`FIESTA`‘s `modGBarea` function generates acre estimates by domain (e.g., Forest type). Calculations are based on Scott et al. 2015 (’Green-Book’) for mapped forest inventory plots. The non-ratio estimator for estimating area by stratum and domain is used. Plots that are totally nonsampled are excluded from the estimation dataset. Next, an adjustment factor is calculated by strata to adjust for nonsampled (nonresponse) conditions that have proportion less than 1. The attribute is the proportion of the plot which is divided by the adjustment factor, and averaged by stratum. Strata means are combined using the strata weights and then expanded to acres using the total land area in the population.

If there are more than one estimation unit (i.e., subpopulation) within the population, estimates are generated by estimation unit. If `sumunits=TRUE`, the estimates and percent standard errors returned are a sum combination of all estimation units. If `rawdata=TRUE`, the raw data returned will include estimates by estimation unit.

Parameters defined in the following examples are organized by category: population data (pop); estimation information (est); and output details (out).

#### POP1: 1.1 Area of forest land, Wyoming, 2011-2013

View Example

Using the `modGBarea` function we generate estimates by estimation unit (i.e., ESTN_UNIT) and sum to population (i.e., WY). `FIESTA` then returns raw data for area of forest land, Wyoming, 2011-2013 (sum estimation units). Note that we set some options for our table output with the `table_opts` argument. For a full list of possible table options, you can run `help(table_options)`.

The following estimates match output from EVALIDator using the WY 2013 Evaluation.

    area1.1 <- modGBarea(
        GBpopdat = GBpopdat,      # pop - population calculations for WY, post-stratification
        landarea = "FOREST",      # est - forest land filter
        sumunits = TRUE,          # est - sum estimation units to population
        )

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area1.1)

output

    ## [1] "est"     "raw"     "statecd" "invyr"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from out outputted list:

    area1.1$est

output

    ##   TOTAL Estimate Percent Sampling Error
    ## 1 Total 10455772                   2.37

We can also look at raw data and titles for estimate, as shown below:

    ## Raw data (list object) for estimate
    raw1.1 <- area1.1$raw        # extract raw data list object from output
    names(raw1.1)

output

    ## [1] "unit_totest" "totest"      "domdat"      "module"      "esttype"
    ## [6] "GBmethod"    "rowvar"      "colvar"      "areaunits"

    head(raw1.1$unit_totest)    # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT       nhat     nhat.var NBRPLT.gt0   ACRES AREAUSED      est
    ## 1         1 0.20844715 4.226522e-04         24 2757613  2757613 574816.6
    ## 2         3 0.10668695 4.474630e-04         12 2021729  2021729 215692.1
    ## 3         5 0.06743421 3.592846e-04         14 3072988  3072988 207224.5
    ## 4         7 0.14429591 7.611799e-05         36 5096959  5096959 735470.3
    ## 5         9 0.09134873 2.718698e-04         18 2729653  2729653 249350.3
    ## 6        11 0.25385133 5.556666e-04         26 1837124  1837124 466356.4
    ##      est.var   est.se     est.cv       pse  CI99left CI99right  CI95left
    ## 1 3214028956 56692.41 0.09862695  9.862695 428786.60  720846.5 463701.49
    ## 2 1828954833 42766.28 0.19827468 19.827468 105533.46  325850.7 131871.73
    ## 3 3392815857 58247.88 0.28108586 28.108586  57187.92  357261.1  93060.77
    ## 4 1977468570 44468.74 0.06046299  6.046299 620926.45  850014.2 648313.21
    ## 5 2025703496 45007.82 0.18050032 18.050032 133417.88  365282.8 161136.63
    ## 6 1875388511 43305.76 0.09285979  9.285979 354808.14  577904.6 381478.66
    ##   CI95right CI68left CI68right
    ## 1  685931.6 518438.4  631194.8
    ## 2  299512.5 173162.8  258221.4
    ## 3  321388.3 149299.5  265149.6
    ## 4  822627.5 691248.0  779692.6
    ## 5  337564.0 204592.0  294108.7
    ## 6  551234.1 423290.6  509422.1

    raw1.1$totest               # estimates for population (i.e., WY)

output

    ##   TOTAL      est     est.var NBRPLT.gt0 AREAUSED   est.se    est.cv     pse
    ## 1     1 10455771 61379281561        556 62600430 247748.4 0.0236949 2.36949
    ##   CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1  9817614  11093929  9970194  10941349 10209396  10702147

#### POP1: 1.2 Area by forest type on forest land, Wyoming, 2011-2013

View Example

In this example, we look at adding rows to the output and include returntitle=TRUE to return title information.

    ## Area of forest land by forest type, Wyoming, 2011-2013
    area1.2 <- modGBarea(
        GBpopdat = GBpopdat,         # pop - population calculations for WY, post-stratification
        landarea = "FOREST",         # est - forest land filter
        rowvar = "FORTYPCD",         # est - row domain
        sumunits = TRUE,             # est - sum estimation units to population
        returntitle = TRUE           # out - return title information
        )

Again, we can look at the contents of the output list. The output now includes titlelst, a list of associated titles.

    names(area1.2)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

And the estimates:

    ## Estimate and percent sampling error of estimate
    area1.2$est

output

    ##    Forest type   Estimate Percent Sampling Error
    ## 1          182   632481.7                  17.28
    ## 2          184   339749.8                  23.85
    ## 3          185    14854.7                    100
    ## 4          201     881189                  14.21
    ## 5          221   889542.8                  12.82
    ## 6          265   467196.7                  19.99
    ## 7          266  1521792.8                  10.41
    ## 8          268   950041.6                  13.55
    ## 9          269      19120                 101.99
    ## 10         281  2483772.2                   7.79
    ## 11         366   236355.9                  28.47
    ## 12         367   362502.8                  22.36
    ## 13         509    95082.1                  45.29
    ## 14         517      19287                 107.34
    ## 15         703    87991.9                  44.35
    ## 16         706    10593.4                    100
    ## 17         901     617017                  17.39
    ## 18         999   827200.1                  14.62
    ## 19       Total 10455771.5                   2.37

Along with raw data and titles:

    ## Raw data (list object) for estimate
    raw1.2 <- area1.2$raw      # extract raw data list object from output
    names(raw1.2)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "domdat"
    ##  [6] "module"      "esttype"     "GBmethod"    "rowvar"      "colvar"
    ## [11] "areaunits"

    head(raw1.2$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT       nhat     nhat.var NBRPLT.gt0   ACRES AREAUSED      est
    ## 1         1 0.20844715 4.226522e-04         24 2757613  2757613 574816.6
    ## 2         3 0.10668695 4.474630e-04         12 2021729  2021729 215692.1
    ## 3         5 0.06743421 3.592846e-04         14 3072988  3072988 207224.5
    ## 4         7 0.14429591 7.611799e-05         36 5096959  5096959 735470.3
    ## 5         9 0.09134873 2.718698e-04         18 2729653  2729653 249350.3
    ## 6        11 0.25385133 5.556666e-04         26 1837124  1837124 466356.4
    ##      est.var   est.se     est.cv       pse  CI99left CI99right  CI95left
    ## 1 3214028956 56692.41 0.09862695  9.862695 428786.60  720846.5 463701.49
    ## 2 1828954833 42766.28 0.19827468 19.827468 105533.46  325850.7 131871.73
    ## 3 3392815857 58247.88 0.28108586 28.108586  57187.92  357261.1  93060.77
    ## 4 1977468570 44468.74 0.06046299  6.046299 620926.45  850014.2 648313.21
    ## 5 2025703496 45007.82 0.18050032 18.050032 133417.88  365282.8 161136.63
    ## 6 1875388511 43305.76 0.09285979  9.285979 354808.14  577904.6 381478.66
    ##   CI95right CI68left CI68right
    ## 1  685931.6 518438.4  631194.8
    ## 2  299512.5 173162.8  258221.4
    ## 3  321388.3 149299.5  265149.6
    ## 4  822627.5 691248.0  779692.6
    ## 5  337564.0 204592.0  294108.7
    ## 6  551234.1 423290.6  509422.1

    raw1.2$totest            # estimates for population (i.e., WY)

output

    ##   TOTAL      est     est.var NBRPLT.gt0 AREAUSED   est.se    est.cv     pse
    ## 1     1 10455771 61379281561        556 62600430 247748.4 0.0236949 2.36949
    ##   CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1  9817614  11093929  9970194  10941349 10209396  10702147

    head(raw1.2$unit_rowest) # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT Forest type       nhat     nhat.var NBRPLT.gt0   ACRES AREAUSED
    ## 1         1         182 0.01428648 0.0001066487          2 2757613  2757613
    ## 2         1         201 0.01023188 0.0000809180          1 2757613  2757613
    ## 3         1         221 0.03239380 0.0002137597          4 2757613  2757613
    ## 4         1         266 0.04092751 0.0002629835          4 2757613  2757613
    ## 5         1         268 0.01023188 0.0000809180          1 2757613  2757613
    ## 6         1         281 0.03854903 0.0002367898          4 2757613  2757613
    ##         est    est.var   est.se    est.cv      pse CI99left CI99right CI95left
    ## 1  39396.59  811002553 28478.11 0.7228571 72.28571        0 112751.34     0.00
    ## 2  28215.56  615335251 24805.95 0.8791587 87.91587        0  92111.45     0.00
    ## 3  89329.58 1625520387 40317.74 0.4513370 45.13370        0 193181.20 10308.25
    ## 4 112862.22 1999839565 44719.57 0.3962315 39.62315        0 228052.19 25213.48
    ## 5  28215.56  615335251 24805.95 0.8791587 87.91587        0  92111.45     0.00
    ## 6 106303.32 1800651626 42434.09 0.3991793 39.91793        0 215606.28 23134.04
    ##   CI95right  CI68left CI68right
    ## 1  95212.66 11076.316  67716.87
    ## 2  76834.33  3547.081  52884.03
    ## 3 168350.90 49235.278 129423.87
    ## 4 200510.96 68390.497 157333.95
    ## 5  76834.33  3547.081  52884.03
    ## 6 189472.60 64104.407 148502.23

    head(raw1.2$rowest)      # estimates by row for population (i.e., WY)

output

    ##   Forest type       est     est.var NBRPLT.gt0 AREAUSED    est.se    est.cv
    ## 1         182 632481.70 11940316027         36 41176360 109271.75 0.1727667
    ## 2         184 339749.83  6565509067         18 24600033  81027.83 0.2384926
    ## 3         185  14854.69   220661757          1  6714319  14854.69 1.0000000
    ## 4         201 881188.96 15674351133         47 27936078 125197.25 0.1420776
    ## 5         221 889542.77 13008784420         51 29512832 114056.06 0.1282187
    ## 6         265 467196.69  8723584461         27 26280468  93400.13 0.1999161
    ##         pse CI99left  CI99right CI95left  CI95right     CI68left  CI68right
    ## 1  17.27667 351016.3  913947.08 418313.0  846650.40 523815.54425  741147.86
    ## 2  23.84926 131036.0  548463.69 180938.2  498561.46 259171.07046  420328.60
    ## 3 100.00000      0.0   53117.83      0.0   43969.34     82.32642   29627.05
    ## 4  14.20776 558702.2 1203675.70 635806.9 1126571.06 756685.57030 1005692.35
    ## 5  12.82187 595753.8 1183331.71 665997.0 1113088.54 776118.82419 1002966.72
    ## 6  19.99161 226613.9  707779.49 284135.8  650257.59 374314.19798  560079.19

    ## Titles (list object) for estimate
    titlelst1.2 <- area1.2$titlelst
    names(titlelst1.2)

output

    ## [1] "title.estpse"  "title.unitvar" "title.ref"     "outfn.estpse"
    ## [5] "outfn.rawdat"  "outfn.param"   "title.rowvar"  "title.row"
    ## [9] "title.unitsn"

    titlelst1.2

output

    ## $title.estpse
    ## [1] "Area, in acres, and percent sampling error on forest land by forest type"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPCD_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPCD_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPCD_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest type; Wyoming, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "acres"

#### POP1: 1.3 Area by forest type and stand-size class on forest land, Wyoming, 2011-2013

View Example

In this example, we look at adding rows and columns to output, including FIA names. We also output estimates and percent standard error in the same cell with the `allin1` argument in `table_options` and save data to an outfolder with the `outfolder` argument in `savedata_options`.

    ## Area of forest land by forest type and stand-size class, Wyoming, 2011-2013
    area1.3 <- modGBarea(
        GBpopdat = GBpopdat,         # pop - population calculations for WY, post-stratification
        landarea = "FOREST",         # est - forest land filter
        rowvar = "FORTYPCD",         # est - row domain
        colvar = "STDSZCD",          # est - column domain
        sumunits = TRUE,             # est - sum estimation units to population
        savedata = TRUE,             # out - save data to outfolder
        returntitle = TRUE,          # out - return title information
        table_opts = list(
          row.FIAname = TRUE,          # table - row domain names
          col.FIAname = TRUE,          # table - column domain names
          allin1 = TRUE                # table - return output with est(pse)
          ),
        savedata_opts = list(
          outfolder = outfolder,       # save - outfolder for saving data
          outfn.pre = "WY"             # save - prefix for output files
          )
        )

We can again look at the output list, estimates, raw data, and titles:

    ## Look at output list
    names(area1.3)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(area1.3$est)

output

    ##                 Forest type     Large diameter    Medium diameter
    ## 1    Rocky Mountain juniper 477,452.6 ( 19.91)  63,177.6 ( 51.17)
    ## 2          Juniper woodland 317,612.4 ( 24.54)        -- (    --)
    ## 3 Pinyon / juniper woodland  14,854.7 (100.00)        -- (    --)
    ## 4               Douglas-fir 589,713.2 ( 17.41) 111,576.9 ( 41.36)
    ## 5            Ponderosa pine 786,751.8 ( 13.52)  46,720.3 ( 60.51)
    ## 6          Engelmann spruce 332,893.7 ( 24.00)  68,400.6 ( 54.85)
    ##       Small diameter  Nonstocked              Total
    ## 1  91,851.5 ( 46.14) -- (    --) 632,481.7 ( 17.28)
    ## 2  22,137.4 (100.00) -- (    --) 339,749.8 ( 23.85)
    ## 3        -- (    --) -- (    --)  14,854.7 (100.00)
    ## 4 179,898.8 ( 31.91) -- (    --) 881,189.0 ( 14.21)
    ## 5  56,070.7 ( 58.39) -- (    --) 889,542.8 ( 12.82)
    ## 6  65,902.4 ( 50.88) -- (    --) 467,196.7 ( 19.99)

    ## Raw data (list object) for estimate
    raw1.3 <- area1.3$raw      # extract raw data list object from output
    names(raw1.3)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "unit_colest"
    ##  [6] "colest"      "unit_grpest" "grpest"      "domdat"      "module"
    ## [11] "esttype"     "GBmethod"    "rowvar"      "colvar"      "areaunits"

    head(raw1.3$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT       nhat     nhat.var NBRPLT.gt0   ACRES AREAUSED      est
    ## 1         1 0.20844715 4.226522e-04         24 2757613  2757613 574816.6
    ## 2         3 0.10668695 4.474630e-04         12 2021729  2021729 215692.1
    ## 3         5 0.06743421 3.592846e-04         14 3072988  3072988 207224.5
    ## 4         7 0.14429591 7.611799e-05         36 5096959  5096959 735470.3
    ## 5         9 0.09134873 2.718698e-04         18 2729653  2729653 249350.3
    ## 6        11 0.25385133 5.556666e-04         26 1837124  1837124 466356.4
    ##      est.var   est.se     est.cv       pse  CI99left CI99right  CI95left
    ## 1 3214028956 56692.41 0.09862695  9.862695 428786.60  720846.5 463701.49
    ## 2 1828954833 42766.28 0.19827468 19.827468 105533.46  325850.7 131871.73
    ## 3 3392815857 58247.88 0.28108586 28.108586  57187.92  357261.1  93060.77
    ## 4 1977468570 44468.74 0.06046299  6.046299 620926.45  850014.2 648313.21
    ## 5 2025703496 45007.82 0.18050032 18.050032 133417.88  365282.8 161136.63
    ## 6 1875388511 43305.76 0.09285979  9.285979 354808.14  577904.6 381478.66
    ##   CI95right CI68left CI68right
    ## 1  685931.6 518438.4  631194.8
    ## 2  299512.5 173162.8  258221.4
    ## 3  321388.3 149299.5  265149.6
    ## 4  822627.5 691248.0  779692.6
    ## 5  337564.0 204592.0  294108.7
    ## 6  551234.1 423290.6  509422.1

    head(raw1.3$totest)      # estimates for population (i.e., WY)

output

    ##   TOTAL      est     est.var NBRPLT.gt0 AREAUSED   est.se    est.cv     pse
    ## 1     1 10455771 61379281561        556 62600430 247748.4 0.0236949 2.36949
    ##   CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1  9817614  11093929  9970194  10941349 10209396  10702147

    head(raw1.3$unit_rowest) # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT                      Forest type       nhat     nhat.var NBRPLT.gt0
    ## 1         1           Rocky Mountain juniper 0.01428648 0.0001066487          2
    ## 2         1                      Douglas-fir 0.01023188 0.0000809180          1
    ## 3         1                   Ponderosa pine 0.03239380 0.0002137597          4
    ## 4         1 Engelmann spruce / subalpine fir 0.04092751 0.0002629835          4
    ## 5         1                    Subalpine fir 0.01023188 0.0000809180          1
    ## 6         1                   Lodgepole pine 0.03854903 0.0002367898          4
    ##   FORTYPCD   ACRES AREAUSED       est    est.var   est.se    est.cv      pse
    ## 1      182 2757613  2757613  39396.59  811002553 28478.11 0.7228571 72.28571
    ## 2      201 2757613  2757613  28215.56  615335251 24805.95 0.8791587 87.91587
    ## 3      221 2757613  2757613  89329.58 1625520387 40317.74 0.4513370 45.13370
    ## 4      266 2757613  2757613 112862.22 1999839565 44719.57 0.3962315 39.62315
    ## 5      268 2757613  2757613  28215.56  615335251 24805.95 0.8791587 87.91587
    ## 6      281 2757613  2757613 106303.32 1800651626 42434.09 0.3991793 39.91793
    ##   CI99left CI99right CI95left CI95right  CI68left CI68right
    ## 1        0 112751.34     0.00  95212.66 11076.316  67716.87
    ## 2        0  92111.45     0.00  76834.33  3547.081  52884.03
    ## 3        0 193181.20 10308.25 168350.90 49235.278 129423.87
    ## 4        0 228052.19 25213.48 200510.96 68390.497 157333.95
    ## 5        0  92111.45     0.00  76834.33  3547.081  52884.03
    ## 6        0 215606.28 23134.04 189472.60 64104.407 148502.23

    head(raw1.3$rowest)      # estimates by row for population (i.e., WY)

output

    ##                 Forest type FORTYPCD       est     est.var NBRPLT.gt0 AREAUSED
    ## 1    Rocky Mountain juniper      182 632481.70 11940316027         36 41176360
    ## 2          Juniper woodland      184 339749.83  6565509067         18 24600033
    ## 3 Pinyon / juniper woodland      185  14854.69   220661757          1  6714319
    ## 4               Douglas-fir      201 881188.96 15674351133         47 27936078
    ## 5            Ponderosa pine      221 889542.77 13008784420         51 29512832
    ## 6          Engelmann spruce      265 467196.69  8723584461         27 26280468
    ##      est.se    est.cv       pse CI99left  CI99right CI95left  CI95right
    ## 1 109271.75 0.1727667  17.27667 351016.3  913947.08 418313.0  846650.40
    ## 2  81027.83 0.2384926  23.84926 131036.0  548463.69 180938.2  498561.46
    ## 3  14854.69 1.0000000 100.00000      0.0   53117.83      0.0   43969.34
    ## 4 125197.25 0.1420776  14.20776 558702.2 1203675.70 635806.9 1126571.06
    ## 5 114056.06 0.1282187  12.82187 595753.8 1183331.71 665997.0 1113088.54
    ## 6  93400.13 0.1999161  19.99161 226613.9  707779.49 284135.8  650257.59
    ##       CI68left  CI68right
    ## 1 523815.54425  741147.86
    ## 2 259171.07046  420328.60
    ## 3     82.32642   29627.05
    ## 4 756685.57030 1005692.35
    ## 5 776118.82419 1002966.72
    ## 6 374314.19798  560079.19

    head(raw1.3$unit_colest) # estimates by column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT Stand-size class        nhat     nhat.var NBRPLT.gt0 STDSZCD
    ## 1         1   Large diameter 0.077375917 4.560626e-04          9       1
    ## 2         1  Medium diameter 0.061391258 3.337868e-04          6       2
    ## 3         1   Small diameter 0.059978760 3.953596e-04          7       3
    ## 4         1       Nonstocked 0.009701211 5.884949e-05          2       5
    ## 5         3   Large diameter 0.083776075 4.661326e-04          9       1
    ## 6         3  Medium diameter 0.007729145 5.975619e-05          1       2
    ##     ACRES AREAUSED       est    est.var   est.se    est.cv       pse CI99left
    ## 1 2757613  2757613 213372.84 3468095619 58890.54 0.2759983  27.59983 61680.86
    ## 2 2757613  2757613 169293.33 2538257910 50381.13 0.2975966  29.75966 39520.15
    ## 3 2757613  2757613 165398.21 3006484369 54831.42 0.3315116  33.15116 24161.84
    ## 4 2757613  2757613  26752.19  447516758 21154.59 0.7907613  79.07613     0.00
    ## 5 2021729  2021729 169372.52 1905264791 43649.34 0.2577120  25.77120 56939.27
    ## 6 2021729  2021729  15626.24  244246754 15628.40 1.0001382 100.01382     0.00
    ##   CI99right CI95left CI95right     CI68left CI68right
    ## 1 365064.81 97949.50 328796.17 154808.67452 271937.00
    ## 2 299066.51 70548.14 268038.53 119191.42369 219395.24
    ## 3 306634.58 57930.61 272865.81 110870.67336 219925.74
    ## 4  81242.80     0.00  68214.42   5714.83513  47789.54
    ## 5 281805.77 83821.39 254923.65 125965.09125 212779.95
    ## 6  55882.32     0.00  46257.33     84.45543  31168.02

    head(raw1.3$colest)      # estimates by column for population (i.e., WY)

output

    ##   Stand-size class STDSZCD       est     est.var NBRPLT.gt0 AREAUSED   est.se
    ## 1   Large diameter       1 5344066.4 64292474661        297 62600430 253559.6
    ## 2  Medium diameter       2 1918907.4 30844444919        104 44542176 175625.9
    ## 3   Small diameter       3 2365597.5 39151933793        127 45447111 197868.5
    ## 4       Nonstocked       5  827200.1 14625718427         53 50085619 120936.8
    ##       est.cv       pse  CI99left CI99right  CI95left CI95right  CI68left
    ## 1 0.04744694  4.744694 4690940.2   5997193 4847098.7   5841034 5091912.1
    ## 2 0.09152389  9.152389 1466525.2   2371290 1574687.1   2263128 1744254.9
    ## 3 0.08364419  8.364419 1855922.1   2875273 1977782.4   2753413 2168825.6
    ## 4 0.14620022 14.620022  515687.5   1138713  590168.3   1064232  706933.5
    ##   CI68right
    ## 1 5596220.8
    ## 2 2093560.0
    ## 3 2562369.4
    ## 4  947466.7

    head(raw1.3$unit_grpest) # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT                      Forest type Stand-size class        nhat
    ## 1         1           Rocky Mountain juniper   Large diameter 0.007143242
    ## 2         1           Rocky Mountain juniper   Small diameter 0.007143242
    ## 3         1                      Douglas-fir   Large diameter 0.010231876
    ## 4         1                   Ponderosa pine   Large diameter 0.032393804
    ## 5         1 Engelmann spruce / subalpine fir   Large diameter 0.020463753
    ## 6         1 Engelmann spruce / subalpine fir  Medium diameter 0.020463753
    ##       nhat.var NBRPLT.gt0 STDSZCD FORTYPCD   ACRES AREAUSED      est    est.var
    ## 1 5.379211e-05          1       1      182 2757613  2757613 19698.30  409058305
    ## 2 5.379211e-05          1       3      182 2757613  2757613 19698.30  409058305
    ## 3 8.091800e-05          1       1      201 2757613  2757613 28215.56  615335251
    ## 4 2.137597e-04          4       1      221 2757613  2757613 89329.58 1625520387
    ## 5 1.517213e-04          2       1      266 2757613  2757613 56431.11 1153753595
    ## 6 1.517213e-04          2       2      266 2757613  2757613 56431.11 1153753595
    ##     est.se    est.cv       pse CI99left CI99right CI95left CI95right  CI68left
    ## 1 20225.19 1.0267481 102.67481        0  71794.93     0.00  59338.94     0.000
    ## 2 20225.19 1.0267481 102.67481        0  71794.93     0.00  59338.94     0.000
    ## 3 24805.95 0.8791587  87.91587        0  92111.45     0.00  76834.33  3547.081
    ## 4 40317.74 0.4513370  45.13370        0 193181.20 10308.25 168350.90 49235.278
    ## 5 33966.95 0.6019188  60.19188        0 143924.17     0.00 123005.11 22652.411
    ## 6 33966.95 0.6019188  60.19188        0 143924.17     0.00 123005.11 22652.411
    ##   CI68right
    ## 1  39811.40
    ## 2  39811.40
    ## 3  52884.03
    ## 4 129423.87
    ## 5  90209.81
    ## 6  90209.81

    head(raw1.3$grpest)      # estimates by row and column for population (i.e., WY)

output

    ##                 Forest type Stand-size class FORTYPCD STDSZCD       est
    ## 1    Rocky Mountain juniper   Large diameter      182       1 477452.63
    ## 2    Rocky Mountain juniper  Medium diameter      182       2  63177.58
    ## 3    Rocky Mountain juniper   Small diameter      182       3  91851.49
    ## 4          Juniper woodland   Large diameter      184       1 317612.44
    ## 5          Juniper woodland   Small diameter      184       3  22137.40
    ## 6 Pinyon / juniper woodland   Large diameter      185       1  14854.69
    ##      est.var NBRPLT.gt0 AREAUSED   est.se    est.cv       pse CI99left
    ## 1 9035011628         28 41176360 95052.68 0.1990829  19.90829 232613.2
    ## 2 1045063815          4 12443792 32327.45 0.5116918  51.16918      0.0
    ## 3 1795723195          5 11719006 42375.97 0.4613532  46.13532      0.0
    ## 4 6075444741         17 23316064 77945.14 0.2454096  24.54096 116839.1
    ## 5  490064326          1  1283969 22137.40 1.0000000 100.00000      0.0
    ## 6  220661757          1  6714319 14854.69 1.0000000 100.00000      0.0
    ##   CI99right   CI95left CI95right     CI68left CI68right
    ## 1 722292.11 291152.806 663752.46 382926.74707 571978.52
    ## 2 146447.56      0.000 126538.21  31029.29567  95325.86
    ## 3 201004.76   8796.105 174906.87  49710.36671 133992.61
    ## 4 518385.81 164842.771 470382.10 240099.27894 395125.60
    ## 5  79159.55      0.000  65525.90    122.68804  44152.11
    ## 6  53117.83      0.000  43969.34     82.32642  29627.05

    ## Titles (list object) for estimate
    titlelst1.3 <- area1.3$titlelst
    names(titlelst1.3)

output

    ##  [1] "title.estpse"  "title.unitvar" "title.ref"     "outfn.estpse"
    ##  [5] "outfn.rawdat"  "outfn.param"   "title.rowvar"  "title.row"
    ##  [9] "title.colvar"  "title.col"     "title.unitsn"

    titlelst1.3

output

    ## $title.estpse
    ## [1] "Area, in acres (percent sampling error), by forest type and stand-size class on forest land"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "WY_area_FORTYPNM_STDSZNM_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "WY_area_FORTYPNM_STDSZNM_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Area, in acres (percent sampling error), by forest type on forest land; Wyoming, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Area, in acres (percent sampling error), by stand-size class on forest land; Wyoming, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "acres"

    ## List output files in outfolder
    list.files(outfolder, pattern = "WY_area")

output

    ## [1] "WY_area_FORTYPNM_STDSZNM_forestland.csv"

    list.files(paste0(outfolder, "/rawdata"), pattern = "WY_area")

output

    ## [1] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata_colest.csv"
    ## [2] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata_domdat.csv"
    ## [3] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata_grpest.csv"
    ## [4] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata_rowest.csv"
    ## [5] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata_totest.csv"
    ## [6] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata_unit_colest.csv"
    ## [7] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata_unit_grpest.csv"
    ## [8] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata_unit_rowest.csv"
    ## [9] "WY_area_FORTYPNM_STDSZNM_forestland_rawdata_unit_totest.csv"

#### POP2: 2.1 Area by forest type and stand-size class, Bighorn National Forest

View Example

Note: Since we only have one estimation unit within the population of interest, we set sumunits=FALSE. Also, we add ref.title to customize the title outputs.

    area2.1 <- modGBarea(
        GBpopdat = GBpopdat.bh,       # pop - population calculations for Bighorn NF, post-stratification
        landarea = "FOREST",          # est - forest land filter
        sumunits = FALSE,             # est - sum estimation units to population
        rowvar = "FORTYPCD",          # est - row domain
        colvar = "STDSZCD",           # est - column domain
        returntitle = TRUE,           # out - return title information
        title_opts = list(
          title.ref = "Bighorn National Forest, 2011-2013"  # title - customize title reference
          ),
        table_opts = list(
          row.FIAname = TRUE,         # table - return FIA row names
          col.FIAname = TRUE          # table - return FIA column names
          )
        )

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area2.1)

output

    ## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "invyr"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from our ouputted list:

    area2.1$est

output

    ##                        Forest type Large diameter Medium diameter
    ## 1                      Douglas-fir        19816.6              --
    ## 2                 Engelmann spruce        45094.9         19816.6
    ## 3 Engelmann spruce / subalpine fir        49541.5          9908.3
    ## 4                    Subalpine fir        24770.8              --
    ## 5                   Lodgepole pine       163994.5        203120.2
    ## 6                            Aspen             --              --
    ## 7                       Nonstocked             --              --
    ## 8                            Total       303218.2        232845.1
    ##   Small diameter Nonstocked    Total
    ## 1        19816.6         --  39633.2
    ## 2             --         --  64911.5
    ## 3         9908.3         --  69358.1
    ## 4        20324.1         --  45094.9
    ## 5        40140.7         -- 407255.4
    ## 6        19816.6         --  19816.6
    ## 7             --    19816.6  19816.6
    ## 8       110006.3    19816.6 665886.2

We can also look at raw data and titles for estimate, as shown below. Note the change in titles.

    ## Raw data (list object) for estimate
    raw2.1 <- area2.1$raw      # extract raw data list object from output
    names(raw2.1)

output

    ##  [1] "unit_totest" "unit_rowest" "unit_colest" "unit_grpest" "domdat"
    ##  [6] "module"      "esttype"     "GBmethod"    "rowvar"      "colvar"
    ## [11] "areaunits"

    head(raw2.1$unit_grpest)  # estimates by row and group domains

output

    ##   ONEUNIT                      Forest type Stand-size class        nhat
    ## 1       1                      Douglas-fir   Large diameter 0.017814471
    ## 2       1                      Douglas-fir   Small diameter 0.017814471
    ## 3       1                 Engelmann spruce   Large diameter 0.040538775
    ## 4       1                 Engelmann spruce  Medium diameter 0.017814471
    ## 5       1 Engelmann spruce / subalpine fir   Large diameter 0.044536178
    ## 6       1 Engelmann spruce / subalpine fir  Medium diameter 0.008907236
    ##       nhat.var NBRPLT.gt0 STDSZCD FORTYPCD ACRES_GIS AREAUSED       est
    ## 1 3.222249e-04          1       1      201   1112388  1112388 19816.606
    ## 2 3.222249e-04          1       3      201   1112388  1112388 19816.606
    ## 3 6.800308e-04          3       1      265   1112388  1112388 45094.851
    ## 4 3.222249e-04          1       2      265   1112388  1112388 19816.606
    ## 5 6.927836e-04          3       1      266   1112388  1112388 49541.514
    ## 6 8.055623e-05          1       2      266   1112388  1112388  9908.303
    ##     est.var    est.se    est.cv       pse CI99left CI99right CI95left CI95right
    ## 1 398723466 19968.061 1.0076428 100.76428        0  71250.92        0  58953.29
    ## 2 398723466 19968.061 1.0076428 100.76428        0  71250.92        0  58953.29
    ## 3 841475072 29008.190 0.6432706  64.32706        0 119815.00        0 101949.86
    ## 4 398723466 19968.061 1.0076428 100.76428        0  71250.92        0  58953.29
    ## 5 857255451 29278.925 0.5909978  59.09978        0 124959.03        0 106927.15
    ## 6  99680866  9984.031 1.0076428 100.76428        0  35625.46        0  29476.64
    ##   CI68left CI68right
    ## 1     0.00  39674.00
    ## 2     0.00  39674.00
    ## 3 16247.43  73942.27
    ## 4     0.00  39674.00
    ## 5 20424.86  78658.17
    ## 6     0.00  19837.00

    ## Titles (list object) for estimate
    titlelst2.1 <- area2.1$titlelst
    names(titlelst2.1)

output

    ##  [1] "title.est"     "title.pse"     "title.unitvar" "title.ref"
    ##  [5] "outfn.estpse"  "outfn.rawdat"  "outfn.param"   "title.rowvar"
    ##  [9] "title.row"     "title.colvar"  "title.col"     "title.unitsn"

    titlelst2.1

output

    ## $title.est
    ## [1] "Area, in acres, on forest land by forest type and stand-size class"
    ##
    ## $title.pse
    ## [1] "Percent sampling error of area, in acres, on forest land by forest type and stand-size class"
    ##
    ## $title.unitvar
    ## [1] "ONEUNIT"
    ##
    ## $title.ref
    ## [1] "Bighorn National Forest, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPNM_STDSZNM_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPNM_STDSZNM_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPNM_STDSZNM_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest type; Bighorn National Forest, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Area, in acres, on forest land by stand-size class; Bighorn National Forest, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "acres"

#### POP2: 2.2 Area by forest type group and primary disturbance class, Bighorn National Forest

View Example

Note: Since we only have one estimation unit within the population of interest, we set sumunits=FALSE. Let’s also add a few more table options to control the forest type groups displayed in the table (rowlut) and fill the NULL values with 0s (estnull).

    area2.2 <- modGBarea(
        GBpopdat = GBpopdat.bh,        # pop - population calculations for Bighorn NF, post-stratification
        landarea = "FOREST",           # est - forest land filter
        sumunits = TRUE,               # est - sum estimation units to population
        rowvar = "FORTYPGRPCD",        # est - row domain
        colvar = "DSTRBCD1",           # est - column domain
        returntitle = TRUE,            # out - return title information
        title_opts = list(
          title.ref = "Bighorn National Forest, 2011-2013"  # title - customize title reference
          ),
        table_opts = list(
          row.FIAname = TRUE,          # table - return FIA row names
          col.FIAname = TRUE,          # table - return FIA column names
          estnull = 0,
          rowlut = c(180, 200, 220, 260, 280, 900, 999),
          raw.keep0 = TRUE
          )
        )

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area2.2)

output

    ## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "invyr"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from our ouputted list:

    area2.2$est

output

    ##                       Forest-type group No visible disturbance Disease
    ## 1                Pinyon / juniper group                      0       0
    ## 2                     Douglas-fir group                39633.2       0
    ## 3                  Ponderosa pine group                      0       0
    ## 4 Fir / spruce / mountain hemlock group               133762.1  4954.2
    ## 5                  Lodgepole pine group               352252.2 35186.5
    ## 6                 Aspen / birch / group                19816.6       0
    ## 7                            Nonstocked                19816.6       0
    ## 8                                 Total               565280.8 40140.7
    ##   Disease to trees    Fire    Wind    Total
    ## 1                0       0       0      0.0
    ## 2                0       0       0  39633.2
    ## 3                0       0       0      0.0
    ## 4          20324.1 20324.1       0 179364.4
    ## 5                0       0 19816.6 407255.4
    ## 6                0       0       0  19816.6
    ## 7                0       0       0  19816.6
    ## 8          20324.1 20324.1 19816.6 665886.2

We can also look at raw data and titles for estimate, as shown below. Note the change in titles.

    ## Raw data (list object) for estimate
    raw2.2 <- area2.2$raw      # extract raw data list object from output
    names(raw2.2)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "unit_colest"
    ##  [6] "colest"      "unit_grpest" "grpest"      "domdat"      "module"
    ## [11] "esttype"     "GBmethod"    "rowvar"      "colvar"      "areaunits"

    head(raw2.2$unit_grpest)  # estimates by row and group domains

output

    ##   ONEUNIT      Forest-type group    Primary disturbance       nhat     nhat.var
    ## 1       1 Pinyon / juniper group No visible disturbance 0.00000000 0.0000000000
    ## 2       1 Pinyon / juniper group                Disease 0.00000000 0.0000000000
    ## 3       1 Pinyon / juniper group       Disease to trees 0.00000000 0.0000000000
    ## 4       1 Pinyon / juniper group                   Fire 0.00000000 0.0000000000
    ## 5       1 Pinyon / juniper group                   Wind 0.00000000 0.0000000000
    ## 6       1      Douglas-fir group No visible disturbance 0.03562894 0.0006283386
    ##   NBRPLT.gt0 FORTYPGRPCD DSTRBCD1 ACRES_GIS AREAUSED      est   est.var
    ## 1          0         180        0   1112388  1112388     0.00         0
    ## 2          0         180       20   1112388  1112388     0.00         0
    ## 3          0         180       22   1112388  1112388     0.00         0
    ## 4          0         180       30   1112388  1112388     0.00         0
    ## 5          0         180       52   1112388  1112388     0.00         0
    ## 6          2         200        0   1112388  1112388 39633.21 777510758
    ##     est.se    est.cv      pse CI99left CI99right CI95left CI95right CI68left
    ## 1     0.00       NaN      NaN        0       0.0        0      0.00     0.00
    ## 2     0.00       NaN      NaN        0       0.0        0      0.00     0.00
    ## 3     0.00       NaN      NaN        0       0.0        0      0.00     0.00
    ## 4     0.00       NaN      NaN        0       0.0        0      0.00     0.00
    ## 5     0.00       NaN      NaN        0       0.0        0      0.00     0.00
    ## 6 27883.88 0.7035483 70.35483        0  111457.3        0  94284.61 11903.87
    ##   CI68right
    ## 1      0.00
    ## 2      0.00
    ## 3      0.00
    ## 4      0.00
    ## 5      0.00
    ## 6  67362.56

    ## Titles (list object) for estimate
    titlelst2.2 <- area2.2$titlelst
    names(titlelst2.2)

output

    ##  [1] "title.est"     "title.pse"     "title.unitvar" "title.ref"
    ##  [5] "outfn.estpse"  "outfn.rawdat"  "outfn.param"   "title.rowvar"
    ##  [9] "title.row"     "title.colvar"  "title.col"     "title.unitsn"

    titlelst2.2

output

    ## $title.est
    ## [1] "Area, in acres, on forest land by forest-type group and primary disturbance"
    ##
    ## $title.pse
    ## [1] "Percent sampling error of area, in acres, on forest land by forest-type group and primary disturbance"
    ##
    ## $title.unitvar
    ## [1] "ONEUNIT"
    ##
    ## $title.ref
    ## [1] "Bighorn National Forest, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPGRPNM_DSTRBNM_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPGRPNM_DSTRBNM_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPGRPNM_DSTRBNM_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest-type group"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest-type group; Bighorn National Forest, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Primary disturbance"
    ##
    ## $title.col
    ## [1] "Area, in acres, on forest land by primary disturbance; Bighorn National Forest, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "acres"

#### POP3: 3.1 Area by forest type group and primary disturbance class, Bighorn National Forest Districts

View Example

In this example, we add a filter to remove from the table output, where there is no visible disturbance. This filter does not change the population data set the estimates are derived from, it only changes the output.

    area3.1 <- modGBarea(
        GBpopdat = GBpopdat.bhdist,    # pop - population calculations for Bighorn NF, post-stratification
        landarea = "FOREST",           # est - forest land filter
        sumunits = TRUE,               # est - sum estimation units to population
        pcfilter = "DSTRBCD1 > 0",     # est - condition filter for table output
        rowvar = "FORTYPGRPCD",        # est - row domain
        colvar = "DSTRBCD1",           # est - column domain
        returntitle = TRUE,            # out - return title information
        title_opts = list(
          title.ref = "Bighorn National Forest, 2011-2013"  # title - customize title reference
          ),
        table_opts = list(
          row.FIAname = TRUE,          # table - return FIA row names
          col.FIAname = TRUE           # table - return FIA column names
          )
        )

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area3.1)

output

    ## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "invyr"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from our ouputted list:

    area3.1$est

output

    ##                       Forest-type group Disease Disease to trees  Fire    Wind
    ## 1 Fir / spruce / mountain hemlock group  6381.8            20165 20165      --
    ## 2                  Lodgepole pine group 39310.4               --    -- 18372.6
    ## 3                                 Total 45692.2            20165 20165 18372.6
    ##      Total
    ## 1  46711.8
    ## 2    57683
    ## 3 104394.8

We can also look at raw data and titles for estimate, as shown below:

    ## Raw data (list object) for estimate
    raw3.1 <- area3.1$raw       # extract raw data list object from output
    names(raw3.1)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "unit_colest"
    ##  [6] "colest"      "unit_grpest" "grpest"      "domdat"      "module"
    ## [11] "esttype"     "GBmethod"    "rowvar"      "colvar"      "areaunits"

    head(raw3.1$unit_rowest)   # estimates by estimation unit for row domains

output

    ##                       DISTRICTNA                     Forest-type group
    ## 1 Medicine Wheel Ranger District Fir / spruce / mountain hemlock group
    ## 2 Medicine Wheel Ranger District                  Lodgepole pine group
    ## 3         Tongue Ranger District                  Lodgepole pine group
    ##         nhat    nhat.var NBRPLT.gt0 FORTYPGRPCD ACRES_GIS AREAUSED      est
    ## 1 0.12814678 0.006624852          3         260  364517.8 364517.8 46711.78
    ## 2 0.10784214 0.006431776          2         280  364517.8 364517.8 39310.38
    ## 3 0.04440298 0.002133087          1         280  413769.7 413769.7 18372.61
    ##     est.var   est.se    est.cv       pse CI99left CI99right CI95left CI95right
    ## 1 880265399 29669.27 0.6351560  63.51560        0 123134.75        0 104862.47
    ## 2 854610742 29233.73 0.7436643  74.36643        0 114611.47        0  96607.43
    ## 3 365195944 19110.10 1.0401408 104.01408        0  67596.97        0  55827.72
    ##   CI68left CI68right
    ## 1 17206.94  76216.62
    ## 2 10238.67  68382.09
    ## 3     0.00  37376.80

    raw3.1$rowest              # estimates for population for row domains

output

    ##                       Forest-type group FORTYPGRPCD      est    est.var
    ## 1 Fir / spruce / mountain hemlock group         260 46711.78  880265399
    ## 2                  Lodgepole pine group         280 57682.99 1219806687
    ##   NBRPLT.gt0 AREAUSED   est.se    est.cv      pse CI99left CI99right CI95left
    ## 1          3 364517.8 29669.27 0.6351560 63.51560        0  123134.7        0
    ## 2          3 778287.5 34925.73 0.6054771 60.54771        0  147645.7        0
    ##   CI95right CI68left CI68right
    ## 1  104862.5 17206.94  76216.62
    ## 2  126136.2 22950.82  92415.16

    head(raw3.1$unit_colest)   # estimates by estimation unit for column domains

output

    ##                       DISTRICTNA Primary disturbance       nhat    nhat.var
    ## 1 Medicine Wheel Ranger District             Disease 0.12534964 0.008479163
    ## 2 Medicine Wheel Ranger District    Disease to trees 0.05531964 0.003799421
    ## 3 Medicine Wheel Ranger District                Fire 0.05531964 0.003799421
    ## 4         Tongue Ranger District                Wind 0.04440298 0.002133087
    ##   NBRPLT.gt0 DSTRBCD1 ACRES_GIS AREAUSED      est    est.var   est.se    est.cv
    ## 1          2       20  364517.8 364517.8 45692.17 1126653662 33565.66 0.7346042
    ## 2          1       22  364517.8 364517.8 20164.99  504841275 22468.67 1.1142416
    ## 3          1       30  364517.8 364517.8 20164.99  504841275 22468.67 1.1142416
    ## 4          1       52  413769.7 413769.7 18372.61  365195944 19110.10 1.0401408
    ##         pse CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1  73.46042        0 132151.59        0 111479.66 12312.54  79071.81
    ## 2 111.42416        0  78040.46        0  64202.78     0.00  42509.14
    ## 3 111.42416        0  78040.46        0  64202.78     0.00  42509.14
    ## 4 104.01408        0  67596.97        0  55827.72     0.00  37376.80

    raw3.1$colest              # estimates for population for column domains

output

    ##   Primary disturbance DSTRBCD1      est    est.var NBRPLT.gt0 AREAUSED   est.se
    ## 1             Disease       20 45692.17 1126653662          2 364517.8 33565.66
    ## 2    Disease to trees       22 20164.99  504841275          1 364517.8 22468.67
    ## 3                Fire       30 20164.99  504841275          1 364517.8 22468.67
    ## 4                Wind       52 18372.61  365195944          1 413769.7 19110.10
    ##      est.cv       pse CI99left CI99right CI95left CI95right CI68left CI68right
    ## 1 0.7346042  73.46042        0 132151.59        0 111479.66 12312.54  79071.81
    ## 2 1.1142416 111.42416        0  78040.46        0  64202.78     0.00  42509.14
    ## 3 1.1142416 111.42416        0  78040.46        0  64202.78     0.00  42509.14
    ## 4 1.0401408 104.01408        0  67596.97        0  55827.72     0.00  37376.80

    ## Titles (list object) for estimate
    titlelst3.1 <- area3.1$titlelst
    names(titlelst3.1)

output

    ##  [1] "title.est"     "title.pse"     "title.unitvar" "title.ref"
    ##  [5] "outfn.estpse"  "outfn.rawdat"  "outfn.param"   "title.rowvar"
    ##  [9] "title.row"     "title.colvar"  "title.col"     "title.unitsn"

    titlelst3.1

output

    ## $title.est
    ## [1] "Area, in acres, on forest land by forest-type group and primary disturbance (DSTRBCD1 > 0)"
    ##
    ## $title.pse
    ## [1] "Percent sampling error of area, in acres, on forest land by forest-type group and primary disturbance (DSTRBCD1 > 0)"
    ##
    ## $title.unitvar
    ## [1] "DISTRICTNA"
    ##
    ## $title.ref
    ## [1] "Bighorn National Forest, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPGRPNM_DSTRBNM_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPGRPNM_DSTRBNM_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPGRPNM_DSTRBNM_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest-type group"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest-type group (DSTRBCD1 > 0); Bighorn National Forest, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Primary disturbance"
    ##
    ## $title.col
    ## [1] "Area, in acres, on forest land by primary disturbance (DSTRBCD1 > 0); Bighorn National Forest, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "acres"

#### POP4: 4.1 Area by forest type group and stand-size class, Rhode Island, 2019

View Example

Note: estimates should match other FIA tools.

    area4.1 <- modGBarea(
        GBpopdat = GBpopdat.RI,        # pop - population calculations for Bighorn NF, post-stratification
        landarea = "FOREST",           # est - forest land filter
        sumunits = TRUE,               # est - sum estimation units to population
        rowvar = "FORTYPCD",        # est - row domain
        colvar = "STDSZCD",            # est - column domain
        returntitle = TRUE,            # out - return title information
        table_opts = list(
          row.FIAname = TRUE,          # table - return FIA row names
          col.FIAname = TRUE           # table - return FIA column names
          )
        )

To get the names of the list components associated with the output of our call of `modGBarea`, we run the following code:

    names(area4.1)

output

    ## [1] "est"      "pse"      "titlelst" "raw"      "statecd"  "invyr"

To easily access our estimate and percent sampling error of estimate we can just grab the `est` object from our ouputted list:

    area4.1$est

output

    ##                                          Forest type Large diameter
    ## 1                                 Eastern white pine        29664.4
    ## 2                                    Eastern hemlock         1015.5
    ## 3                                         Pitch pine        11079.8
    ## 4  Eastern white pine / northern red oak / white ash        11494.2
    ## 5                              Other pine / hardwood         1267.4
    ## 6                                       Chestnut oak             --
    ## 7                      White oak / red oak / hickory        75625.8
    ## 8                                          White oak        11354.2
    ## 9                                   Northern red oak        31766.7
    ## 10      Yellow-poplar / white oak / northern red oak         3745.7
    ## 11                                       Scarlet oak         6384.2
    ## 12            Chestnut oak / black oak / scarlet oak        31237.2
    ## 13                                   Red maple / oak        15559.7
    ## 14                            Mixed upland hardwoods         4800.7
    ## 15               Sweetbay / swamp tupelo / red maple        18424.4
    ## 16                       Silver maple / American elm         1411.5
    ## 17                               Red maple / lowland         3187.3
    ## 18                Sugar maple / beech / yellow birch           8744
    ## 19                                      Black cherry         2304.4
    ## 20                                Red maple / upland         3745.7
    ## 21                                             Aspen         1990.2
    ## 22                                   Other hardwoods         1942.8
    ## 23                                        Nonstocked             --
    ## 24                                             Total       276745.7
    ##    Medium diameter Small diameter Nonstocked    Total
    ## 1               --             --         --  29664.4
    ## 2               --             --         --   1015.5
    ## 3               --             --         --  11079.8
    ## 4           2888.3             --         --  14382.6
    ## 5               --             --         --   1267.4
    ## 6           3745.7             --         --   3745.7
    ## 7          22315.7             --         --  97941.5
    ## 8            653.5             --         --  12007.7
    ## 9               --             --         --  31766.7
    ## 10              --             --         --   3745.7
    ## 11         19346.9             --         --  25731.1
    ## 12           337.8             --         --    31575
    ## 13            1569             --         --  17128.7
    ## 14              --             --         --   4800.7
    ## 15          7108.3             --         --  25532.8
    ## 16              --             --         --   1411.5
    ## 17         10300.1             --         --  13487.4
    ## 18              --             --         --     8744
    ## 19              --             --         --   2304.4
    ## 20          5086.7             --         --   8832.4
    ## 21              --           3000         --   4990.1
    ## 22          3745.7          936.4         --   6624.9
    ## 23              --             --     3347.5   3347.5
    ## 24         77097.8         3936.4     3347.5 361127.4

We can also look at raw data and titles for estimate, as shown below. Note the change in titles.

    ## Raw data (list object) for estimate
    raw4.1 <- area4.1$raw      # extract raw data list object from output
    names(raw4.1)

output

    ##  [1] "unit_totest" "totest"      "unit_rowest" "rowest"      "unit_colest"
    ##  [6] "colest"      "unit_grpest" "grpest"      "domdat"      "module"
    ## [11] "esttype"     "GBmethod"    "rowvar"      "colvar"      "areaunits"

    head(raw4.1$unit_grpest)  # estimates by row and group domains

output

    ##   ESTN_UNIT                                       Forest type Stand-size class
    ## 1         2                                Eastern white pine   Large diameter
    ## 2         2                                   Eastern hemlock   Large diameter
    ## 3         2                                        Pitch pine   Large diameter
    ## 4         2 Eastern white pine / northern red oak / white ash   Large diameter
    ## 5         2 Eastern white pine / northern red oak / white ash  Medium diameter
    ## 6         2                             Other pine / hardwood   Large diameter
    ##          nhat     nhat.var NBRPLT.gt0 STDSZCD FORTYPCD AREA_USED AREAUSED
    ## 1 0.026662405 1.600966e-04          4       1      103  568453.6 568453.6
    ## 2 0.001786383 2.880391e-06          1       1      105  568453.6 568453.6
    ## 3 0.006389858 3.795914e-05          1       1      167  568453.6 568453.6
    ## 4 0.014878180 7.034373e-05          4       1      401  568453.6 568453.6
    ## 5 0.005081007 1.297166e-05          2       2      401  568453.6 568453.6
    ## 6 0.002229531 2.996697e-06          2       1      409  568453.6 568453.6
    ##         est    est.var    est.se    est.cv      pse CI99left CI99right CI95left
    ## 1 15156.340 51733547.1 7192.6036 0.4745607 47.45607        0 33683.259 1059.096
    ## 2  1015.476   930768.0  964.7632 0.9500603 95.00603        0  3500.541    0.000
    ## 3  3632.338 12266097.9 3502.2989 0.9641997 96.41997        0 12653.662    0.000
    ## 4  8457.555 22730838.2 4767.6869 0.5637193 56.37193        0 20738.302    0.000
    ## 5  2888.317  4191655.5 2047.3533 0.7088396 70.88396        0  8161.949    0.000
    ## 6  1267.385   968351.2  984.0484 0.7764399 77.64399        0  3802.126    0.000
    ##   CI95right   CI68left CI68right
    ## 1 29253.584 8003.59845 22309.081
    ## 2  2906.377   56.05943  1974.892
    ## 3 10496.717  149.44889  7115.226
    ## 4 17802.049 3716.29112 13198.819
    ## 5  6901.056  852.31018  4924.323
    ## 6  3196.085  288.79044  2245.980

    ## Titles (list object) for estimate
    titlelst4.1 <- area4.1$titlelst
    names(titlelst4.1)

output

    ##  [1] "title.est"     "title.pse"     "title.unitvar" "title.ref"
    ##  [5] "outfn.estpse"  "outfn.rawdat"  "outfn.param"   "title.rowvar"
    ##  [9] "title.row"     "title.colvar"  "title.col"     "title.unitsn"

    titlelst4.1

output

    ## $title.est
    ## [1] "Area, in acres, on forest land by forest type and stand-size class"
    ##
    ## $title.pse
    ## [1] "Percent sampling error of area, in acres, on forest land by forest type and stand-size class"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] "Rhode Island, 2013-2019"
    ##
    ## $outfn.estpse
    ## [1] "area_FORTYPNM_STDSZNM_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "area_FORTYPNM_STDSZNM_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "area_FORTYPNM_STDSZNM_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Area, in acres, on forest land by forest type; Rhode Island, 2013-2019"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Area, in acres, on forest land by stand-size class; Rhode Island, 2013-2019"
    ##
    ## $title.unitsn
    ## [1] "acres"

### modGBtree

`FIESTA`‘s `modGBtree` function generates tree estimates by domain (e.g., Forest type) and/or tree domain (e.g., Species). Calculations are based on Scott et al. 2005 (’the green-book’) for mapped forest inventory plots. The non-ratio estimator for estimating tree attributes by stratum and domain is used. Plots that are totally nonsampled are excluded from estimation dataset. Next, an adjustment factor is calculated by strata to adjust for nonsampled (nonresponse) conditions that have proportion less than 1. Attributes adjusted to a per-acre value are summed by plot, divided by the adjustment factor, and averaged by stratum. Strata means are combined using the strata weights and then expanded to using the total land area in the population.

If there are more than one estimation unit (i.e., subpopulation) within the population, estimates are generated by estimation unit. If `sumunits = TRUE`, the estimates and percent standard errors returned are a sum combination of all estimation units. If `rawdata = TRUE`, the raw data returned will include estimates by estimation unit.

Parameters defined in the following examples are organized by category: population data (pop); estimation information (est); and output details (out).

The following reference table can be used for defining `estvar` and `estvar.filter`:

    FIESTAutils::ref_estvar[, c("ESTTITLE", "ESTVAR", "ESTFILTER", "ESTUNITS")]

output

    ##                                                             ESTTITLE    ESTVAR
    ## 1                                Number of trees (at least 1 in dia) TPA_UNADJ
    ## 2                           Number of live trees (at least 1 in dia) TPA_UNADJ
    ## 3                           Number of live trees (at least 5 in dia) TPA_UNADJ
    ## 4                  Number of standing-dead trees (at least 5 in dia) TPA_UNADJ
    ## 5                      Number of growing-stock trees (at least 5 in) TPA_UNADJ
    ## 6                                 Number of saplings (1 to 5 in dia) TPA_UNADJ
    ## 7                       Basal area of live trees (at least 1 in dia)        BA
    ## 8                       Basal area of live trees (at least 5 in dia)        BA
    ## 9                       Basal area of dead trees (at least 5 in dia)        BA
    ## 10                            Basal area of saplings (1 to 5 in dia)        BA
    ## 11                           Net volume of trees (at least 5 in dia)  VOLCFNET
    ## 12                      Net volume of live trees (at least 5 in dia)  VOLCFNET
    ## 13             Net volume of growing-stock trees (at least 5 in dia)  VOLCFNET
    ## 14             Net volume of standing-dead trees (at least 5 in dia)  VOLCFNET
    ## 15              Net volume in the sawlog portion of a sawtimber tree  VOLCSNET
    ## 16                                     Net volume of sawtimber trees  VOLBFNET
    ## 17                         Gross volume of trees (at least 5 in dia)  VOLCFGRS
    ## 18                    Gross volume of live trees (at least 5 in dia)  VOLCFGRS
    ## 19           Gross volume of growing-stock trees (at least 5 in dia)  VOLCFGRS
    ## 20           Gross volume of standing-dead trees (at least 5 in dia)  VOLCFGRS
    ## 21            Gross volume in the sawlog portion of a sawtimber tree  VOLCSGRS
    ## 22               Aboveground dry weight of trees (at least 1 in dia) DRYBIO_AG
    ## 23          Aboveground dry weight of live trees (at least 1 in dia) DRYBIO_AG
    ## 24          Aboveground dry weight of live trees (at least 5 in dia) DRYBIO_AG
    ## 25 Aboveground dry weight of standing-dead trees (at least 5 in dia) DRYBIO_AG
    ## 26                Aboveground dry weight of saplings (1 to 5 in dia) DRYBIO_AG
    ## 27              Aboveground carbon in live trees (at least 1 in dia) CARBON_AG
    ## 28              Aboveground carbon in live trees (at least 5 in dia) CARBON_AG
    ## 29     Aboveground carbon in standing-dead trees (at least 5 in dia) CARBON_AG
    ## 30                                                          Tree age   TREEAGE
    ## 31                          Number of seedlings (less than 1 in dia) TPA_UNADJ
    ## 32                             Number of trees (including seedlings) TPA_UNADJ
    ##                                             ESTFILTER
    ## 1                                          DIA >= 1.0
    ## 2                          STATUSCD == 1 & DIA >= 1.0
    ## 3                          STATUSCD == 1 & DIA >= 5.0
    ## 4  STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 5                          TREECLCD == 2 & DIA >= 5.0
    ## 6                                          DIA <  5.0
    ## 7                                       STATUSCD == 1
    ## 8                          STATUSCD == 1 & DIA >= 5.0
    ## 9  STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 10                                         DIA <  5.0
    ## 11                                         DIA >= 5.0
    ## 12                         STATUSCD == 1 & DIA >= 5.0
    ## 13                         TREECLCD == 2 & DIA >= 5.0
    ## 14 STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 15                                      STATUSCD == 1
    ## 16         TREECLCD == 2 & STATUSCD == 1 & DIA >= 9.0
    ## 17                                         DIA >= 5.0
    ## 18                         STATUSCD == 1 & DIA >= 5.0
    ## 19                         TREECLCD == 2 & DIA >= 5.0
    ## 20 STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 21                                      STATUSCD == 1
    ## 22                                         DIA >= 1.0
    ## 23                                      STATUSCD == 1
    ## 24                         STATUSCD == 1 & DIA >= 5.0
    ## 25 STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 26                                         DIA <  5.0
    ## 27                                      STATUSCD == 1
    ## 28                         STATUSCD == 1 & DIA >= 5.0
    ## 29 STATUSCD == 2 & STANDING_DEAD_CD == 1 & DIA >= 5.0
    ## 30
    ## 31
    ## 32
    ##                                    ESTUNITS
    ## 1                                     trees
    ## 2                                     trees
    ## 3                                     trees
    ## 4                                     trees
    ## 5                                     trees
    ## 6                                     trees
    ## 7                               square feet
    ## 8                               square feet
    ## 9                               square feet
    ## 10                              square feet
    ## 11                               cubic feet
    ## 12                               cubic feet
    ## 13                               cubic feet
    ## 14                               cubic feet
    ## 15                               cubic feet
    ## 16 board feet (International 1/4-inch rule)
    ## 17                               cubic feet
    ## 18                               cubic feet
    ## 19                               cubic feet
    ## 20                               cubic feet
    ## 21                               cubic feet
    ## 22                                     tons
    ## 23                                     tons
    ## 24                                     tons
    ## 25                                     tons
    ## 26                                     tons
    ## 27                                     tons
    ## 28                                     tons
    ## 29                                     tons
    ## 30                                    years
    ## 31                                seedlings
    ## 32                                    trees

#### POP1: 1.1 Net cubic-foot volume of live trees, Wyoming, 2011-2013

View Example

Now, we can generate estimates by estimation unit (i.e., ESTN_UNIT) and sum to population (i.e., WY) with `modGBtree`:

    ## Return raw data and titles
    ## Total net cubic-foot volume of live trees (at least 5 inches diameter), Wyoming, 2011-2013
    tree1.1 <- modGBtree(
        GBpopdat = GBpopdat,         # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "VOLCFNET",               # est - net cubic-foot volume
        estvar.filter = "STATUSCD == 1",   # est - live trees only
        returntitle = TRUE           # out - return title information
        )

We can now take a look at the output list, estimates and percent sampling errors, raw data, and titles:

    ## Look at output list
    names(tree1.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree1.1$est

output

    ##   TOTAL    Estimate Percent Sampling Error
    ## 1 Total 15375870681                   4.62

    ## Raw data (list object) for estimate
    raw1.1 <- tree1.1$raw      # extract raw data list object from output
    names(raw1.1)

output

    ##  [1] "unit_totest"   "totest"        "domdat"        "estvar"
    ##  [5] "estvar.filter" "module"        "esttype"       "GBmethod"
    ##  [9] "rowvar"        "colvar"        "areaunits"     "estunits"

    head(raw1.1$unit_totest)   # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat  nhat.var NBRPLT.gt0   ACRES AREAUSED        est
    ## 1         1 195.73873  651.5668         24 2757613  2757613  539771676
    ## 2         3 261.09190 5469.0924         11 2021729  2021729  527857071
    ## 3         5  28.20704  133.7263         14 3072988  3072988   86679888
    ## 4         7 248.65595  711.3799         35 5096959  5096959 1267389205
    ## 5         9  72.68797  595.6953         15 2729653  2729653  198412942
    ## 6        11 361.12987 4806.0717         26 1837124  1837124  663440342
    ##        est.var    est.se    est.cv      pse  CI99left  CI99right   CI95left
    ## 1 4.954793e+15  70390294 0.1304075 13.04075 358458294  721085058  401809235
    ## 2 2.235430e+16 149513555 0.2832463 28.32463 142735673  912978468  234815887
    ## 3 1.262812e+15  35536060 0.4099689 40.99689         0  178214712   17030491
    ## 4 1.848093e+16 135944594 0.1072635 10.72635 917219136 1617559274 1000942696
    ## 5 4.438529e+15  66622284 0.3357759 33.57759  26805311  370020574   67835665
    ## 6 1.622061e+16 127360159 0.1919693 19.19693 335382313  991498372  413819018
    ##    CI95right   CI68left  CI68right
    ## 1  677734117  469771493  609771859
    ## 2  820898255  379172137  676542004
    ## 3  156329285   51340773  122019003
    ## 4 1533835713 1132198032 1402580378
    ## 5  328990220  132159887  264665998
    ## 6  913061667  536786028  790094656

    head(raw1.1$totest)        # estimates for population (i.e., WY)

output

    ##   TOTAL         est     est.var NBRPLT.gt0 AREAUSED    est.se     est.cv
    ## 1     1 15375870681 5.05387e+17        501 62600430 710905765 0.04623516
    ##        pse    CI99left   CI99right    CI95left   CI95right    CI68left
    ## 1 4.623516 13544698781 17207042582 13982520986 16769220377 14668904840
    ##     CI68right
    ## 1 16082836523

    ## Titles (list object) for estimate
    titlelst1.1 <- tree1.1$titlelst
    names(titlelst1.1)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.unitvar"
    ##  [5] "title.ref"     "outfn.estpse"  "outfn.rawdat"  "outfn.param"
    ##  [9] "title.tot"     "title.unitsn"

    titlelst1.1

output

    ## $title.estpse
    ## [1] "Net volume of live trees (at least 5 in dia), in cubic feet, and percent sampling error on forest land live"
    ##
    ## $title.yvar
    ## [1] "Net volume, in cubic feet"
    ##
    ## $title.estvar
    ## [1] "Net volume of live trees (at least 5 in dia)"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "tree_VOLCFNET_live_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "tree_VOLCFNET_live_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "tree_VOLCFNET_live_forestland_parameters"
    ##
    ## $title.tot
    ## [1] "Net volume of live trees (at least 5 in dia), in cubic feet, on forest land live; Wyoming, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "cubic feet"

#### POP1: 1.2 Net cubic-foot volume of live trees by forest type, Wyoming, 2011-2013

View Example

This example adds rows to the output for net cubic-foot volume of live trees (at least 5 inches diameter) by forest type, Wyoming, 2011-2013. We also choose to return titles with `returntitle = TRUE`.

    tree1.2 <- modGBtree(
        GBpopdat = GBpopdat,         # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "VOLCFNET",               # est - net cubic-foot volume
        estvar.filter = "STATUSCD == 1",   # est - live trees only
        rowvar = "FORTYPCD",         # est - row domain
        returntitle = TRUE           # out - return title information
        )

Again, we investigate the output of the returned list:

    ## Look at output list
    names(tree1.2)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree1.2$est

output

    ##    Forest type      Estimate Percent Sampling Error
    ## 1          182   242440123.2                  26.49
    ## 2          184     230107430                  30.86
    ## 3          185     6261387.5                    100
    ## 4          201  1490656736.8                  20.03
    ## 5          221  1198569953.2                  15.58
    ## 6          265    1465809264                  26.98
    ## 7          266  3524923701.7                  12.54
    ## 8          268  1691926936.3                  16.49
    ## 9          269    55531261.1                 101.99
    ## 10         281    4395706727                  10.63
    ## 11         366    63477062.1                  48.94
    ## 12         367   274487533.2                  27.71
    ## 13         509    35654050.9                  64.85
    ## 14         517     2715581.1                 107.34
    ## 15         703   191973135.9                  60.52
    ## 16         706     4928705.3                    100
    ## 17         901     442778101                  27.76
    ## 18         999    57922990.9                  43.48
    ## 19       Total 15375870681.4                   4.62

    ## Raw data (list object) for estimate
    raw1.2 <- tree1.2$raw      # extract raw data list object from output
    names(raw1.2)

output

    ##  [1] "unit_totest"   "totest"        "unit_rowest"   "rowest"
    ##  [5] "domdat"        "estvar"        "estvar.filter" "module"
    ##  [9] "esttype"       "GBmethod"      "rowvar"        "colvar"
    ## [13] "areaunits"     "estunits"

    head(raw1.2$unit_totest)   # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat  nhat.var NBRPLT.gt0   ACRES AREAUSED        est
    ## 1         1 195.73873  651.5668         24 2757613  2757613  539771676
    ## 2         3 261.09190 5469.0924         11 2021729  2021729  527857071
    ## 3         5  28.20704  133.7263         14 3072988  3072988   86679888
    ## 4         7 248.65595  711.3799         35 5096959  5096959 1267389205
    ## 5         9  72.68797  595.6953         15 2729653  2729653  198412942
    ## 6        11 361.12987 4806.0717         26 1837124  1837124  663440342
    ##        est.var    est.se    est.cv      pse  CI99left  CI99right   CI95left
    ## 1 4.954793e+15  70390294 0.1304075 13.04075 358458294  721085058  401809235
    ## 2 2.235430e+16 149513555 0.2832463 28.32463 142735673  912978468  234815887
    ## 3 1.262812e+15  35536060 0.4099689 40.99689         0  178214712   17030491
    ## 4 1.848093e+16 135944594 0.1072635 10.72635 917219136 1617559274 1000942696
    ## 5 4.438529e+15  66622284 0.3357759 33.57759  26805311  370020574   67835665
    ## 6 1.622061e+16 127360159 0.1919693 19.19693 335382313  991498372  413819018
    ##    CI95right   CI68left  CI68right
    ## 1  677734117  469771493  609771859
    ## 2  820898255  379172137  676542004
    ## 3  156329285   51340773  122019003
    ## 4 1533835713 1132198032 1402580378
    ## 5  328990220  132159887  264665998
    ## 6  913061667  536786028  790094656

    head(raw1.2$totest)        # estimates for population (i.e., WY)

output

    ##   TOTAL         est     est.var NBRPLT.gt0 AREAUSED    est.se     est.cv
    ## 1     1 15375870681 5.05387e+17        501 62600430 710905765 0.04623516
    ##        pse    CI99left   CI99right    CI95left   CI95right    CI68left
    ## 1 4.623516 13544698781 17207042582 13982520986 16769220377 14668904840
    ##     CI68right
    ## 1 16082836523

    head(raw1.2$unit_rowest)   # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT Forest type      nhat   nhat.var NBRPLT.gt0   ACRES AREAUSED
    ## 1         1         182  1.840465   1.865846          2 2757613  2757613
    ## 2         1         201 17.516131 237.143344          1 2757613  2757613
    ## 3         1         221 34.018474 253.098906          4 2757613  2757613
    ## 4         1         266 70.935057 823.576489          4 2757613  2757613
    ## 5         1         268  9.434619  68.799185          1 2757613  2757613
    ## 6         1         281 47.532459 511.473146          4 2757613  2757613
    ##         est      est.var   est.se    est.cv      pse CI99left CI99right
    ## 1   5075289 1.418869e+13  3766788 0.7421820 74.21820        0  14777893
    ## 2  48302709 1.803340e+15 42465749 0.8791587 87.91587        0 157687230
    ## 3  93809786 1.924673e+15 43871093 0.4676601 46.76601        0 206814233
    ## 4 195611436 6.262829e+15 79138040 0.4045676 40.45676        0 399457517
    ## 5  26017027 5.231785e+14 22873097 0.8791587 87.91587        0  84934220
    ## 6 131076128 3.889461e+15 62365547 0.4757964 47.57964        0 291719132
    ##   CI95left CI95right  CI68left CI68right
    ## 1        0  12458058   1329377   8821201
    ## 2        0 131534048   6072311  90533108
    ## 3  7824025 179795548  50181832 137437740
    ## 4 40503728 350719143 116911988 274310883
    ## 5        0  70847473   3270696  48763358
    ## 6  8841902 253310354  69056218 193096038

    head(raw1.2$rowest)        # estimates by row for population (i.e., WY)

output

    ##   Forest type        est      est.var NBRPLT.gt0 AREAUSED    est.se    est.cv
    ## 1         182  242440123 4.123987e+15         34 41176360  64218277 0.2648830
    ## 2         184  230107430 5.041156e+15         18 24600033  71001100 0.3085563
    ## 3         185    6261387 3.920497e+13          1  6714319   6261387 1.0000000
    ## 4         201 1490656737 8.911894e+16         47 27936078 298527962 0.2002661
    ## 5         221 1198569953 3.486594e+16         51 29512832 186724229 0.1557892
    ## 6         265 1465809264 1.563783e+17         26 26280468 395446949 0.2697806
    ##         pse  CI99left  CI99right  CI95left  CI95right     CI68left  CI68right
    ## 1  26.48830  77024803  407855444 116574613  368305634 1.785778e+08  306302495
    ## 2  30.85563  47220716  412994144  90947831  369267029 1.594998e+08  300715034
    ## 3 100.00000         0   22389653         0   18533481 3.470134e+04   12488074
    ## 4  20.02661 721699665 2259613808 905552684 2075760790 1.193783e+09 1787530222
    ## 5  15.57892 717600213 1679539693 832597190 1564542716 1.012881e+09 1384259334
    ## 6  26.97806 447205424 2484413104 690747486 2240871042 1.072554e+09 1859064600

    ## Titles (list object) for estimate
    titlelst1.2 <- tree1.2$titlelst
    names(titlelst1.2)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.unitvar"
    ##  [5] "title.ref"     "outfn.estpse"  "outfn.rawdat"  "outfn.param"
    ##  [9] "title.rowvar"  "title.row"     "title.unitsn"

    titlelst1.2

output

    ## $title.estpse
    ## [1] "Net volume of live trees (at least 5 in dia), in cubic feet, and percent sampling error on forest land by forest type live"
    ##
    ## $title.yvar
    ## [1] "Net volume, in cubic feet"
    ##
    ## $title.estvar
    ## [1] "Net volume of live trees (at least 5 in dia)"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "tree_VOLCFNET_live_FORTYPCD_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "tree_VOLCFNET_live_FORTYPCD_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "tree_VOLCFNET_live_FORTYPCD_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Net volume of live trees (at least 5 in dia), in cubic feet, on forest land by forest type live; Wyoming, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "cubic feet"

We can also create a simple barplot from the output:

    ## Create barplot
    datBarplot(
          raw1.2$unit_rowest,
          xvar = titlelst1.2$title.rowvar,
          yvar = "est"
          )

plot

And a fancier barplot:

    ## Create fancier barplot
    datBarplot(
          raw1.2$unit_rowest,
          xvar = titlelst1.2$title.rowvar,
          yvar = "est",
          errbars = TRUE,
          sevar = "est.se",
          main = FIESTAutils::wraptitle(titlelst1.2$title.row, 75),
          ylabel = titlelst1.2$title.yvar,
          divideby = "million"
          )

plot

#### POP1: 1.3 Net cubic-foot volume of live trees by forest type and stand-size class, Wyoming, 2011-2013

View Example

This examples adds rows and columns to the output, including FIA names, for net cubic-foot volume of live trees (at least 5 inches diameter) by forest type and stand-size class, Wyoming, 2011-2013. We also use the `*_options` functions to return output with estimates (est) and percent standard error (pse) in same cell - est(pse) with `allin1 = TRUE` and save data to an outfolder with `savedata = TRUE` and `outfolder = outfolder`.

    tree1.3 <- modGBtree(
        GBpopdat = GBpopdat,         # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "VOLCFNET",               # est - net cubic-foot volume
        estvar.filter = "STATUSCD  == 1",   # est - live trees only
        rowvar = "FORTYPCD",         # est - row domain
        colvar = "STDSZCD",          # est - column domain
        returntitle = TRUE,          # out - return title information
        savedata = TRUE,             # out - save data to outfolder
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          col.FIAname = TRUE,          # est - column domain names
          allin1 = TRUE                # out - return output with est(pse)
        ),
        savedata_opts = savedata_options(
          outfolder = outfolder,       # out - outfolder for saving data
          outfn.pre = "WY"             # out - prefix for output files
          )
        )

Again, we investigate the output of the returned list:

    ## Look at output list from modGBarea()
    names(tree1.3)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree1.3$est

output

    ##                                 Forest type            Large diameter
    ## 1                    Rocky Mountain juniper    214,721,618.4 ( 28.83)
    ## 2                          Juniper woodland    229,692,847.7 ( 30.91)
    ## 3                 Pinyon / juniper woodland      6,261,387.5 (100.00)
    ## 4                               Douglas-fir  1,334,902,603.8 ( 22.03)
    ## 5                            Ponderosa pine  1,139,547,783.7 ( 16.42)
    ## 6                          Engelmann spruce  1,281,632,109.2 ( 29.93)
    ## 7          Engelmann spruce / subalpine fir  2,875,027,387.9 ( 14.78)
    ## 8                             Subalpine fir  1,373,528,320.2 ( 19.27)
    ## 9                               Blue spruce     55,531,261.1 (101.99)
    ## 10                           Lodgepole pine  2,630,976,759.3 ( 15.47)
    ## 11                              Limber pine     45,808,964.0 ( 65.79)
    ## 12                           Whitebark pine    137,971,439.1 ( 45.56)
    ## 13                                  Bur oak     20,692,056.0 (107.34)
    ## 14                 Elm / ash / black locust               -- (    --)
    ## 15                               Cottonwood    188,406,692.6 ( 61.65)
    ## 16 Sugarberry / hackberry / elm / green ash               -- (    --)
    ## 17                                    Aspen     74,838,473.2 ( 50.03)
    ## 18                               Nonstocked               -- (    --)
    ## 19                                    Total 11,609,539,703.8 (  6.19)
    ##             Medium diameter         Small diameter            Nonstocked
    ## 1     22,385,041.3 ( 63.25)   5,333,463.5 ( 69.90)           -- (    --)
    ## 2               -- (    --)     414,582.3 (100.00)           -- (    --)
    ## 3               -- (    --)            -- (    --)           -- (    --)
    ## 4    104,576,458.3 ( 46.27)  51,177,674.7 ( 53.46)           -- (    --)
    ## 5     52,902,315.4 ( 69.18)   6,119,854.1 ( 86.33)           -- (    --)
    ## 6    148,466,505.1 ( 65.62)  35,710,649.8 ( 70.33)           -- (    --)
    ## 7    484,400,541.2 ( 28.35) 165,495,772.6 ( 37.68)           -- (    --)
    ## 8    252,705,478.8 ( 38.70)  65,693,137.3 ( 40.60)           -- (    --)
    ## 9               -- (    --)            -- (    --)           -- (    --)
    ## 10 1,571,475,627.4 ( 16.27) 193,254,340.3 ( 29.58)           -- (    --)
    ## 11              -- (    --)  17,668,098.2 ( 43.62)           -- (    --)
    ## 12    92,141,580.6 ( 43.32)  44,374,513.6 ( 49.86)           -- (    --)
    ## 13     8,618,754.9 ( 72.30)   6,343,240.0 ( 74.49)           -- (    --)
    ## 14              -- (    --)   2,715,581.1 (107.34)           -- (    --)
    ## 15     1,482,050.0 (102.34)   2,084,393.2 (102.13)           -- (    --)
    ## 16     4,928,705.3 (100.00)            -- (    --)           -- (    --)
    ## 17   247,309,802.8 ( 45.04) 120,629,824.9 ( 32.93)           -- (    --)
    ## 18              -- (    --)            -- (    --) 57,922,990.9 ( 43.48)
    ## 19 2,991,392,861.2 ( 11.12) 717,015,125.5 ( 14.47) 57,922,990.9 ( 43.48)
    ##                        Total
    ## 1     242,440,123.2 ( 26.49)
    ## 2     230,107,430.0 ( 30.86)
    ## 3       6,261,387.5 (100.00)
    ## 4   1,490,656,736.8 ( 20.03)
    ## 5   1,198,569,953.2 ( 15.58)
    ## 6   1,465,809,264.0 ( 26.98)
    ## 7   3,524,923,701.7 ( 12.54)
    ## 8   1,691,926,936.3 ( 16.49)
    ## 9      55,531,261.1 (101.99)
    ## 10  4,395,706,727.0 ( 10.63)
    ## 11     63,477,062.1 ( 48.94)
    ## 12    274,487,533.2 ( 27.71)
    ## 13     35,654,050.9 ( 64.85)
    ## 14      2,715,581.1 (107.34)
    ## 15    191,973,135.9 ( 60.52)
    ## 16      4,928,705.3 (100.00)
    ## 17    442,778,101.0 ( 27.76)
    ## 18     57,922,990.9 ( 43.48)
    ## 19 15,375,870,681.4 (  4.62)

    ## Raw data (list object) for estimate
    raw1.3 <- tree1.3$raw      # extract raw data list object from output
    names(raw1.3)

output

    ##  [1] "unit_totest"   "totest"        "unit_rowest"   "rowest"
    ##  [5] "unit_colest"   "colest"        "unit_grpest"   "grpest"
    ##  [9] "domdat"        "estvar"        "estvar.filter" "module"
    ## [13] "esttype"       "GBmethod"      "rowvar"        "colvar"
    ## [17] "areaunits"     "estunits"

    head(raw1.3$unit_totest)   # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat  nhat.var NBRPLT.gt0   ACRES AREAUSED        est
    ## 1         1 195.73873  651.5668         24 2757613  2757613  539771676
    ## 2         3 261.09190 5469.0924         11 2021729  2021729  527857071
    ## 3         5  28.20704  133.7263         14 3072988  3072988   86679888
    ## 4         7 248.65595  711.3799         35 5096959  5096959 1267389205
    ## 5         9  72.68797  595.6953         15 2729653  2729653  198412942
    ## 6        11 361.12987 4806.0717         26 1837124  1837124  663440342
    ##        est.var    est.se    est.cv      pse  CI99left  CI99right   CI95left
    ## 1 4.954793e+15  70390294 0.1304075 13.04075 358458294  721085058  401809235
    ## 2 2.235430e+16 149513555 0.2832463 28.32463 142735673  912978468  234815887
    ## 3 1.262812e+15  35536060 0.4099689 40.99689         0  178214712   17030491
    ## 4 1.848093e+16 135944594 0.1072635 10.72635 917219136 1617559274 1000942696
    ## 5 4.438529e+15  66622284 0.3357759 33.57759  26805311  370020574   67835665
    ## 6 1.622061e+16 127360159 0.1919693 19.19693 335382313  991498372  413819018
    ##    CI95right   CI68left  CI68right
    ## 1  677734117  469771493  609771859
    ## 2  820898255  379172137  676542004
    ## 3  156329285   51340773  122019003
    ## 4 1533835713 1132198032 1402580378
    ## 5  328990220  132159887  264665998
    ## 6  913061667  536786028  790094656

    head(raw1.3$totest)        # estimates for population (i.e., WY)

output

    ##   TOTAL         est     est.var NBRPLT.gt0 AREAUSED    est.se     est.cv
    ## 1     1 15375870681 5.05387e+17        501 62600430 710905765 0.04623516
    ##        pse    CI99left   CI99right    CI95left   CI95right    CI68left
    ## 1 4.623516 13544698781 17207042582 13982520986 16769220377 14668904840
    ##     CI68right
    ## 1 16082836523

    head(raw1.3$unit_rowest)   # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT                      Forest type      nhat   nhat.var NBRPLT.gt0
    ## 1         1           Rocky Mountain juniper  1.840465   1.865846          2
    ## 2         1                      Douglas-fir 17.516131 237.143344          1
    ## 3         1                   Ponderosa pine 34.018474 253.098906          4
    ## 4         1 Engelmann spruce / subalpine fir 70.935057 823.576489          4
    ## 5         1                    Subalpine fir  9.434619  68.799185          1
    ## 6         1                   Lodgepole pine 47.532459 511.473146          4
    ##   FORTYPCD   ACRES AREAUSED       est      est.var   est.se    est.cv      pse
    ## 1      182 2757613  2757613   5075289 1.418869e+13  3766788 0.7421820 74.21820
    ## 2      201 2757613  2757613  48302709 1.803340e+15 42465749 0.8791587 87.91587
    ## 3      221 2757613  2757613  93809786 1.924673e+15 43871093 0.4676601 46.76601
    ## 4      266 2757613  2757613 195611436 6.262829e+15 79138040 0.4045676 40.45676
    ## 5      268 2757613  2757613  26017027 5.231785e+14 22873097 0.8791587 87.91587
    ## 6      281 2757613  2757613 131076128 3.889461e+15 62365547 0.4757964 47.57964
    ##   CI99left CI99right CI95left CI95right  CI68left CI68right
    ## 1        0  14777893        0  12458058   1329377   8821201
    ## 2        0 157687230        0 131534048   6072311  90533108
    ## 3        0 206814233  7824025 179795548  50181832 137437740
    ## 4        0 399457517 40503728 350719143 116911988 274310883
    ## 5        0  84934220        0  70847473   3270696  48763358
    ## 6        0 291719132  8841902 253310354  69056218 193096038

    head(raw1.3$rowest)        # estimates by row for population (i.e., WY)

output

    ##                 Forest type FORTYPCD        est      est.var NBRPLT.gt0
    ## 1    Rocky Mountain juniper      182  242440123 4.123987e+15         34
    ## 2          Juniper woodland      184  230107430 5.041156e+15         18
    ## 3 Pinyon / juniper woodland      185    6261387 3.920497e+13          1
    ## 4               Douglas-fir      201 1490656737 8.911894e+16         47
    ## 5            Ponderosa pine      221 1198569953 3.486594e+16         51
    ## 6          Engelmann spruce      265 1465809264 1.563783e+17         26
    ##   AREAUSED    est.se    est.cv       pse  CI99left  CI99right  CI95left
    ## 1 41176360  64218277 0.2648830  26.48830  77024803  407855444 116574613
    ## 2 24600033  71001100 0.3085563  30.85563  47220716  412994144  90947831
    ## 3  6714319   6261387 1.0000000 100.00000         0   22389653         0
    ## 4 27936078 298527962 0.2002661  20.02661 721699665 2259613808 905552684
    ## 5 29512832 186724229 0.1557892  15.57892 717600213 1679539693 832597190
    ## 6 26280468 395446949 0.2697806  26.97806 447205424 2484413104 690747486
    ##    CI95right     CI68left  CI68right
    ## 1  368305634 1.785778e+08  306302495
    ## 2  369267029 1.594998e+08  300715034
    ## 3   18533481 3.470134e+04   12488074
    ## 4 2075760790 1.193783e+09 1787530222
    ## 5 1564542716 1.012881e+09 1384259334
    ## 6 2240871042 1.072554e+09 1859064600

    head(raw1.3$unit_colest)   # estimates by column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT Stand-size class       nhat    nhat.var NBRPLT.gt0 STDSZCD   ACRES
    ## 1         1   Large diameter  94.016742  855.249292          9       1 2757613
    ## 2         1  Medium diameter  86.699781  700.129293          6       2 2757613
    ## 3         1   Small diameter  12.535570   29.278708          7       3 2757613
    ## 4         1       Nonstocked   2.486640    6.001976          2       5 2757613
    ## 5         3   Large diameter 254.300842 5435.088191          9       1 2021729
    ## 6         3  Medium diameter   6.693154   44.810694          1       2 2021729
    ##   AREAUSED       est      est.var    est.se    est.cv       pse  CI99left
    ## 1  2757613 259261790 6.503683e+15  80645415 0.3110578  31.10578  51532967
    ## 2  2757613 239084444 5.324084e+15  72966320 0.3051906  30.51906  51135659
    ## 3  2757613  34568252 2.226479e+14  14921390 0.4316501  43.16501         0
    ## 4  2757613   6857191 4.564161e+13   6755857 0.9852223  98.52223         0
    ## 5  2021729 514127388 2.221532e+16 149048029 0.2899049  28.99049 130205106
    ## 6  2021729  13531744 1.831587e+14  13533614 1.0001382 100.01382         0
    ##   CI99right  CI95left CI95right     CI68left CI68right
    ## 1 466990612 101199682 417323898 179063321.47 339460258
    ## 2 427033229  96073085 382095803 166522511.89 311646376
    ## 3  73003204   5322866  63813638  19729558.20  49406945
    ## 4  24259125         0  20098427    138775.13  13575606
    ## 5 898049669 221998618 806256157 365905399.76 662349375
    ## 6  48392023         0  40057139     73135.29  26990353

    head(raw1.3$colest)        # estimates by column for population (i.e., WY)

output

    ##   Stand-size class STDSZCD         est      est.var NBRPLT.gt0 AREAUSED
    ## 1   Large diameter       1 11609539704 5.161343e+17        297 62600430
    ## 2  Medium diameter       2  2991392861 1.105610e+17        103 44542176
    ## 3   Small diameter       3   717015126 1.076318e+16         98 45447111
    ## 4       Nonstocked       5    57922991 6.341877e+14         20 50085619
    ##      est.se     est.cv       pse   CI99left   CI99right    CI95left   CI95right
    ## 1 718424845 0.06188228  6.188228 9758999937 13460079471 10201452883 13017626525
    ## 2 332507087 0.11115460 11.115460 2134911362  3847874361  2339690945  3643094777
    ## 3 103745759 0.14469117 14.469117  449783759   984246492   513677174   920353077
    ## 4  25183083 0.43476835 43.476835          0   122790315     8565055   107280927
    ##      CI68left   CI68right
    ## 1 10895096454 12323982954
    ## 2  2660728567  3322057155
    ## 3   613844337   820185914
    ## 4    32879475    82966507

    head(raw1.3$unit_grpest)   # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT                      Forest type Stand-size class       nhat
    ## 1         1           Rocky Mountain juniper   Large diameter  0.7078824
    ## 2         1           Rocky Mountain juniper   Small diameter  1.1325822
    ## 3         1                      Douglas-fir   Large diameter 17.5161306
    ## 4         1                   Ponderosa pine   Large diameter 34.0184741
    ## 5         1 Engelmann spruce / subalpine fir   Large diameter 41.0423590
    ## 6         1 Engelmann spruce / subalpine fir  Medium diameter 29.8926981
    ##      nhat.var NBRPLT.gt0 STDSZCD FORTYPCD   ACRES AREAUSED       est
    ## 1   0.5282628          1       1      182 2757613  2757613   1952066
    ## 2   1.3522822          1       3      182 2757613  2757613   3123223
    ## 3 237.1433437          1       1      201 2757613  2757613  48302709
    ## 4 253.0989058          4       1      221 2757613  2757613  93809786
    ## 5 615.7209615          2       1      266 2757613  2757613 113178943
    ## 6 326.3892799          2       2      266 2757613  2757613  82432493
    ##        est.var   est.se    est.cv       pse CI99left CI99right CI95left
    ## 1 4.017137e+12  2004280 1.0267481 102.67481        0   7114748        0
    ## 2 1.028333e+13  3206764 1.0267481 102.67481        0  11383300        0
    ## 3 1.803340e+15 42465749 0.8791587  87.91587        0 157687230        0
    ## 4 1.924673e+15 43871093 0.4676601  46.76601        0 206814233  7824025
    ## 5 4.682207e+15 68426651 0.6045882  60.45882        0 289434316        0
    ## 6 2.482004e+15 49819718 0.6043699  60.43699        0 210759581        0
    ##   CI95right CI68left CI68right
    ## 1   5880382        0   3945237
    ## 2   9408365        0   6312215
    ## 3 131534048  6072311  90533108
    ## 4 179795548 50181832 137437740
    ## 5 247292715 45131520 181226366
    ## 6 180077345 32888882 131976104

    head(raw1.3$grpest)        # estimates by row and column for population (i.e., WY)

output

    ##                 Forest type Stand-size class FORTYPCD STDSZCD         est
    ## 1    Rocky Mountain juniper   Large diameter      182       1 214721618.4
    ## 2    Rocky Mountain juniper  Medium diameter      182       2  22385041.3
    ## 3    Rocky Mountain juniper   Small diameter      182       3   5333463.5
    ## 4          Juniper woodland   Large diameter      184       1 229692847.7
    ## 5          Juniper woodland   Small diameter      184       3    414582.3
    ## 6 Pinyon / juniper woodland   Large diameter      185       1   6261387.5
    ##        est.var NBRPLT.gt0 AREAUSED     est.se    est.cv       pse CI99left
    ## 1 3.831831e+15         28 41176360 61901783.0 0.2882885  28.82885 55273192
    ## 2 2.004547e+14          4 12443792 14158202.6 0.6324850  63.24850        0
    ## 3 1.389858e+13          3 11719006  3728080.2 0.6989980  69.89980        0
    ## 4 5.040984e+15         17 23316064 70999889.5 0.3091080  30.91080 46809252
    ## 5 1.718785e+11          1  1283969   414582.3 1.0000000 100.00000        0
    ## 6 3.920497e+13          1  6714319  6261387.5 1.0000000 100.00000        0
    ##   CI99right CI95left CI95right     CI68left   CI68right
    ## 1 374170045 93396353 336046884 1.531629e+08 276280334.5
    ## 2  58854154        0  50134608 8.305305e+06  36464777.5
    ## 3  14936362        0  12640366 1.626045e+06   9040882.2
    ## 4 412576444 90535621 368850074 1.590864e+08 300299247.5
    ## 5   1482476        0   1227149 2.297664e+03    826866.9
    ## 6  22389653        0  18533481 3.470134e+04  12488073.6

    ## Titles (list object) for estimate
    titlelst1.3 <- tree1.3$titlelst
    names(titlelst1.3)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.unitvar"
    ##  [5] "title.ref"     "outfn.estpse"  "outfn.rawdat"  "outfn.param"
    ##  [9] "title.rowvar"  "title.row"     "title.colvar"  "title.col"
    ## [13] "title.unitsn"

    titlelst1.3

output

    ## $title.estpse
    ## [1] "Net volume of live trees (at least 5 in dia), in cubic feet (percent sampling error), by forest type and stand-size class on forest land live"
    ##
    ## $title.yvar
    ## [1] "Net volume, in cubic feet"
    ##
    ## $title.estvar
    ## [1] "Net volume of live trees (at least 5 in dia)"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Net volume of live trees (at least 5 in dia), in cubic feet (percent sampling error), by forest type on forest land live; Wyoming, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Net volume of live trees (at least 5 in dia), in cubic feet (percent sampling error), by stand-size class on forest land live; Wyoming, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "cubic feet"

    ## List output files in outfolder
    list.files(outfolder, pattern = "WY_tree")

output

    ## [1] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland.csv"

    list.files(paste0(outfolder, "/rawdata"), pattern = "WY_tree")

output

    ## [1] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata_colest.csv"
    ## [2] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata_domdat.csv"
    ## [3] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata_grpest.csv"
    ## [4] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata_rowest.csv"
    ## [5] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata_totest.csv"
    ## [6] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata_unit_colest.csv"
    ## [7] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata_unit_grpest.csv"
    ## [8] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata_unit_rowest.csv"
    ## [9] "WY_tree_VOLCFNET_live_FORTYPNM_STDSZNM_forestland_rawdata_unit_totest.csv"

#### POP1: 1.4 Number of live trees by species, Wyoming, 2011-2013

View Example

We can use tree domain in estimation output rows:

    ## Number of live trees (at least 1 inch diameter) by species
    tree1.4 <- modGBtree(
        GBpopdat = GBpopdat,         # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "TPA_UNADJ",               # est - number of trees per acre
        estvar.filter = "STATUSCD == 1",    # est - live trees only
        rowvar = "SPCD",             # est - row domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = FALSE               # out - return output with est and pse
          )
        )

We can also look at the output list and estimates again:

    ## Look at output list
    names(tree1.4)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree1.4$est

output

    ##                        Species     Estimate Percent Sampling Error
    ## 1                    green ash    1288927.2                  91.98
    ## 2            plains cottonwood    2027267.7                  82.62
    ## 3                quaking aspen  253876469.3                  18.88
    ## 4        narrowleaf cottonwood    4585746.3                   57.8
    ## 5                  limber pine   93252401.1                   20.6
    ## 6               whitebark pine  237604255.7                  15.55
    ## 7  common or two-needle pinyon     119194.9                    100
    ## 8               lodgepole pine 1458559075.4                  11.36
    ## 9                subalpine fir 1149064733.7                   8.33
    ## 10            Engelmann spruce  472342493.8                  11.91
    ## 11              ponderosa pine  260988183.6                  15.71
    ## 12                Utah juniper   45255337.4                  27.67
    ## 13      Rocky Mountain juniper    100653677                  20.77
    ## 14                 blue spruce    6731266.5                  63.82
    ## 15                     bur oak   23604682.4                   38.4
    ## 16  curlleaf mountain-mahogany       538261                  69.47
    ## 17                 paper birch    1445853.6                 107.34
    ## 18                    boxelder    1126869.5                  81.93
    ## 19                 Douglas-fir  206129937.1                  19.48
    ## 20                       Total 4319194633.1                   4.83

#### POP1: 1.5 Number of live trees (plus seedlings) by species, Wyoming, 2011-2013

View Example

We can also add seedlings.

Note: seedling data are only available for number of trees (estvar = TPA_UNADJ).

Note: must include seedling data in population data calculations.

    ## Number of live trees by species, including seedlings
    tree1.5 <- modGBtree(
        GBpopdat = GBpopdat,         # pop - population calculations
        estseed = "add",             # est - add seedling data
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "TPA_UNADJ",               # est - number of trees per acre
        estvar.filter = "STATUSCD == 1",    # est - live trees only
        rowvar = "SPCD",             # est - row domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = FALSE,              # out - return output with est and pse
          )
        )

And again we can look at our outputs and compare estimates:

    ## Look at output list
    names(tree1.5)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree1.5$est

output

    ##                        Species     Estimate Percent Sampling Error
    ## 1                    green ash    4180634.4                  79.48
    ## 2            plains cottonwood    2027267.7                  82.62
    ## 3                quaking aspen   1870428252                  16.35
    ## 4        narrowleaf cottonwood   51593597.2                  61.75
    ## 5                  limber pine  465590561.1                  19.17
    ## 6               whitebark pine 1409480054.7                  14.49
    ## 7  common or two-needle pinyon     119194.9                    100
    ## 8               lodgepole pine 4417708152.9                  14.09
    ## 9                subalpine fir 7595942259.7                   8.12
    ## 10            Engelmann spruce 1653336667.8                   10.4
    ## 11              ponderosa pine  628036828.7                  21.73
    ## 12                Utah juniper   51536534.7                  27.08
    ## 13      Rocky Mountain juniper  149789361.8                  20.65
    ## 14                 blue spruce    9597932.5                  72.35
    ## 15                     bur oak  367717832.1                  50.87
    ## 16  curlleaf mountain-mahogany       538261                  69.47
    ## 17                 paper birch     10120975                   75.3
    ## 18                    boxelder    5891677.3                  96.11
    ## 19                 Douglas-fir  547823866.7                  24.23
    ## 20                       Total  19241459912                   5.21

    ## Compare estimates with and without seedlings
    head(tree1.4$est)

output

    ##                 Species    Estimate Percent Sampling Error
    ## 1             green ash   1288927.2                  91.98
    ## 2     plains cottonwood   2027267.7                  82.62
    ## 3         quaking aspen 253876469.3                  18.88
    ## 4 narrowleaf cottonwood   4585746.3                   57.8
    ## 5           limber pine  93252401.1                   20.6
    ## 6        whitebark pine 237604255.7                  15.55

    head(tree1.5$est)

output

    ##                 Species     Estimate Percent Sampling Error
    ## 1             green ash    4180634.4                  79.48
    ## 2     plains cottonwood    2027267.7                  82.62
    ## 3         quaking aspen   1870428252                  16.35
    ## 4 narrowleaf cottonwood   51593597.2                  61.75
    ## 5           limber pine  465590561.1                  19.17
    ## 6        whitebark pine 1409480054.7                  14.49

#### POP1: 1.6 Number of seedlings by species, Wyoming, 2011-2013

View Example

Of course, we can also look at only seedlings.

Note: seedling data are only available for number of trees (estvar = TPA_UNADJ).

Note: must include seedling data in population data calculations.

    ## Number of live trees seedlings by species
    tree1.6 <- modGBtree(
        GBpopdat = GBpopdat,         # pop - population calculations
        estseed = "only",            # est - add seedling data
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "TPA_UNADJ",        # est - number of trees per acre
        rowvar = "SPCD",             # est - row domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = FALSE               # out - return output with est and pse
          )
        )

And again we can look at our outputs and compare estimates:

    ## Look at output list
    names(tree1.6)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree1.6$est

output

    ##                        Species     Estimate Percent Sampling Error
    ## 1                    green ash    1288927.2                  91.98
    ## 2            plains cottonwood    2154301.9                  77.88
    ## 3                quaking aspen  280590243.3                  17.75
    ## 4        narrowleaf cottonwood    4703050.5                  58.39
    ## 5                  limber pine  122214988.3                  17.62
    ## 6               whitebark pine  314094044.8                  13.78
    ## 7  common or two-needle pinyon     119194.9                    100
    ## 8               lodgepole pine 1705072743.5                     10
    ## 9                subalpine fir 1246233705.1                   8.04
    ## 10            Engelmann spruce  554005425.4                  11.01
    ## 11              ponderosa pine  274484174.5                  15.47
    ## 12                Utah juniper   50965393.6                  26.72
    ## 13      Rocky Mountain juniper  105031772.7                  20.34
    ## 14                 blue spruce    6731266.5                  63.82
    ## 15                     bur oak   24110714.8                  37.92
    ## 16  curlleaf mountain-mahogany     670280.1                  70.07
    ## 17                 paper birch    1561923.5                  99.39
    ## 18                    boxelder    1244044.7                  77.19
    ## 19                 Douglas-fir  249338016.3                  17.75
    ## 20                       Total 4944614211.6                   4.37

    ## Compare estimates with, without, and only seedlings
    head(tree1.4$est)

output

    ##                 Species    Estimate Percent Sampling Error
    ## 1             green ash   1288927.2                  91.98
    ## 2     plains cottonwood   2027267.7                  82.62
    ## 3         quaking aspen 253876469.3                  18.88
    ## 4 narrowleaf cottonwood   4585746.3                   57.8
    ## 5           limber pine  93252401.1                   20.6
    ## 6        whitebark pine 237604255.7                  15.55

    head(tree1.5$est)

output

    ##                 Species     Estimate Percent Sampling Error
    ## 1             green ash    4180634.4                  79.48
    ## 2     plains cottonwood    2027267.7                  82.62
    ## 3         quaking aspen   1870428252                  16.35
    ## 4 narrowleaf cottonwood   51593597.2                  61.75
    ## 5           limber pine  465590561.1                  19.17
    ## 6        whitebark pine 1409480054.7                  14.49

    head(tree1.6$est)

output

    ##                 Species    Estimate Percent Sampling Error
    ## 1             green ash   1288927.2                  91.98
    ## 2     plains cottonwood   2154301.9                  77.88
    ## 3         quaking aspen 280590243.3                  17.75
    ## 4 narrowleaf cottonwood   4703050.5                  58.39
    ## 5           limber pine 122214988.3                  17.62
    ## 6        whitebark pine 314094044.8                  13.78

#### POP2: 2.1 Number of live trees by forest type and species on forest land, Bighorn National Forest

View Example

We can also use tree domain in estimation output columns:

    ## First, we can save our table options for the next few examples
    tab_opts <- table_options(
          row.FIAname = TRUE,          # est - row domain names
          col.FIAname = TRUE,          # est - column domain names
          allin1 = TRUE                # out - return output with est(pse)
          )

    ## Number of live trees (at least 1 inch diameter) by forest type and species
    tree2.1 <- modGBtree(
        GBpopdat = GBpopdat.bh,      # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "TPA_UNADJ",               # est - number of trees per acre
        estvar.filter = "STATUSCD == 1",    # est - live trees only
        rowvar = "FORTYPCD",         # est - row domain
        colvar = "SPCD",             # est - column domain
        returntitle = TRUE,          # out - return title information
        table_opts = tab_opts
        )

And we can see our output:

    ## Look at output list
    names(tree2.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(tree2.1$est)

output

    ##                        Forest type         subalpine fir      Engelmann spruce
    ## 1                      Douglas-fir           -- (    --)           -- (    --)
    ## 2                 Engelmann spruce  5,749,765.4 ( 77.01) 13,619,107.7 ( 52.63)
    ## 3 Engelmann spruce / subalpine fir 27,053,651.4 ( 64.52) 12,143,606.5 ( 60.75)
    ## 4                    Subalpine fir 11,179,232.3 ( 86.25)    119,257.2 (100.76)
    ## 5                   Lodgepole pine 27,358,852.8 ( 36.77) 28,613,223.4 ( 44.96)
    ## 6                            Aspen           -- (    --)           -- (    --)
    ##           lodgepole pine          limber pine           Douglas-fir
    ## 1            -- (    --) 4,282,944.6 ( 97.93) 21,220,362.7 ( 89.14)
    ## 2   4,640,716.3 ( 98.14)          -- (    --)  3,512,930.6 (100.76)
    ## 3            -- (    --)   357,771.7 (100.76)  1,431,086.9 ( 79.06)
    ## 4   2,504,402.2 ( 79.86)          -- (    --)           -- (    --)
    ## 5 158,980,399.8 ( 22.59)          -- (    --)    238,514.5 (100.76)
    ## 6  11,884,459.5 (100.76)          -- (    --)           -- (    --)
    ##           quaking aspen                  Total
    ## 1           -- (    --)  25,503,307.3 ( 90.54)
    ## 2           -- (    --)  27,522,520.0 ( 50.80)
    ## 3           -- (    --)  40,986,116.6 ( 55.71)
    ## 4           -- (    --)  13,802,891.7 ( 85.10)
    ## 5 10,051,446.6 ( 84.39) 225,242,437.0 ( 19.47)
    ## 6           -- (    --)  11,884,459.5 (100.76)

#### POP2: 2.2 Net cubic-foot volume of standing dead trees by species and cause of death on forest land, Bighorn National Forest

View Example

We can also examine dead trees with the filter `estvar.filter = "STATUSCD == 2 & STANDING_DEAD_CD == 1"`.

    ## Net cubic-foot volume of dead trees (at least 5 inches diameter) by species and cause of death,
    ##    Wyoming, 2011-2013
    tree2.2 <- modGBtree(
        GBpopdat = GBpopdat.bh,      # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "VOLCFNET",         # est - number of trees per acre
        estvar.filter = "STATUSCD == 2 & STANDING_DEAD_CD == 1",    # est - standing dead trees only
        rowvar = "SPCD",             # est - row domain
        colvar = "AGENTCD",          # est - column domain
        returntitle = TRUE,          # out - return title information
        table_opts = tab_opts
        )

And we can see our output of dead trees:

    ## Look at output list
    names(tree2.2)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(tree2.2$est)

output

    ##            Species                  Other                Insect
    ## 1      limber pine      80,021.0 (100.76)           -- (    --)
    ## 2   lodgepole pine  60,852,105.6 ( 29.62)  4,117,810.2 ( 66.17)
    ## 3    subalpine fir  21,189,023.2 ( 49.41)  2,541,990.5 (100.76)
    ## 4 Engelmann spruce  41,510,499.0 ( 46.15) 55,078,805.3 ( 97.75)
    ## 5      Douglas-fir  13,987,315.2 (100.76)           -- (    --)
    ## 6            Total 137,618,964.1 ( 30.03) 61,738,605.9 ( 91.29)
    ##                 Disease                  Fire  Unidentified animal
    ## 1           -- (    --)  3,003,576.6 (100.76)          -- (    --)
    ## 2  1,673,271.3 (100.76)           -- (    --) 3,119,322.2 ( 84.42)
    ## 3 30,508,246.9 ( 62.45)  2,659,270.4 (101.17)          -- (    --)
    ## 4  2,289,354.2 ( 69.14) 32,926,712.4 (101.17)          -- (    --)
    ## 5           -- (    --)           -- (    --)          -- (    --)
    ## 6 34,470,872.4 ( 61.02) 38,589,559.4 ( 93.63) 3,119,322.2 ( 84.42)
    ##                Weather Vegetation (e.g., suppression)          Unidentified
    ## 1          -- (    --)                    -- (    --)    128,219.3 (100.76)
    ## 2   729,991.4 ( 97.76)                    -- (    --) 12,650,879.8 ( 62.27)
    ## 3   538,443.6 (101.17)             276,703.4 (100.76)  7,869,019.1 ( 66.63)
    ## 4   610,506.0 (100.76)                    -- (    --)  8,282,613.5 ( 78.51)
    ## 5          -- (    --)                    -- (    --)           -- (    --)
    ## 6 1,878,940.9 ( 57.37)             276,703.4 (100.76) 28,930,731.8 ( 40.25)
    ##                    Total
    ## 1   3,211,816.9 ( 96.72)
    ## 2  83,143,380.4 ( 29.20)
    ## 3  65,582,697.1 ( 44.71)
    ## 4 140,698,490.4 ( 50.62)
    ## 5  13,987,315.2 (100.76)
    ## 6 306,623,700.2 ( 30.22)

#### Adding diameter classes to population tree data (for `modGBtree` and `modGBratio` examples 7-9)

View Example

    ## Look at tree data in GBpopdat.bh
    head(GBpopdat.bh$treex)

output

    ##            PLT_CN CONDID SUBP TREE STATUSCD SPCD SPGRPCD  DIA HT TREECLCD
    ## 1: 40404876010690      1    1    9        1   19      12 12.1 64        2
    ## 2: 40404876010690      1    1   15        1   19      12  1.5 11        2
    ## 3: 40404876010690      1    2   18        2   19      12 12.0 57        3
    ## 4: 40404876010690      1    2   22        2   19      12  5.5 53        3
    ## 5: 40404876010690      1    2   26        1   19      12  1.8 12        2
    ## 6: 40404876010690      1    4    3        1   19      12  6.9 46        2
    ##    AGENTCD STANDING_DEAD_CD  VOLCFNET  VOLCFGRS VOLBFNET TPA_UNADJ  DRYBIO_AG
    ## 1:      NA               NA 20.908857 20.908857 104.7298  6.018046 581.902600
    ## 2:      NA               NA        NA        NA       NA 74.965282   2.410252
    ## 3:      70                1  6.374361 18.212460       NA  6.018046 167.502609
    ## 4:      NA                1  2.889440  2.889440       NA  6.018046  67.437885
    ## 5:      NA               NA        NA        NA       NA 74.965282   3.925288
    ## 6:      NA               NA  4.250839  4.250839       NA  6.018046 122.432583
    ##     CARBON_AG         BA TPROP_BASIS  tadjfac
    ## 1: 290.951300 0.79852014        SUBP 1.006135
    ## 2:   1.205126 0.01227150        MICR 1.006135
    ## 3:  83.751305 0.78537600        SUBP 1.006135
    ## 4:  33.718943 0.16498350        SUBP 1.006135
    ## 5:   1.962644 0.01767096        MICR 1.006135
    ## 6:  61.216292 0.25966494        SUBP 1.006135

    ## Use reference data frame stored as an R object in FIESTA
    head(FIESTAutils::ref_diacl2in)

output

    ##   MIN  MAX  DIACL2IN
    ## 1   1  2.9   1.0-2.9
    ## 2   3  4.9   3.0-4.9
    ## 3   5  6.9   5.0-6.9
    ## 4   7  8.9   7.0-8.9
    ## 5   9 10.9  9.0-10.9
    ## 6  11 12.9 11.0-12.9

    ## Appends a new column to GBpopdat$treex classifying the DIA variable based on MIN and MAX columns in ref_diacl2in
    dat <- datLUTclass(x = GBpopdat.bh$treex,
                       xvar = "DIA",
                       LUT = FIESTAutils::ref_diacl2in,
                       LUTclassnm = "DIACL2IN")
    GBpopdat.bh$treex <- dat$xLUT

    ## Look at tree data, with new column (DIACL2IN)
    head(GBpopdat.bh$treex)

output

    ##           PLT_CN CONDID SUBP TREE STATUSCD SPCD SPGRPCD  DIA HT TREECLCD
    ## 1 40404876010690      1    1    9        1   19      12 12.1 64        2
    ## 2 40404876010690      1    1   15        1   19      12  1.5 11        2
    ## 3 40404876010690      1    2   18        2   19      12 12.0 57        3
    ## 4 40404876010690      1    2   22        2   19      12  5.5 53        3
    ## 5 40404876010690      1    2   26        1   19      12  1.8 12        2
    ## 6 40404876010690      1    4    3        1   19      12  6.9 46        2
    ##   AGENTCD STANDING_DEAD_CD  VOLCFNET  VOLCFGRS VOLBFNET TPA_UNADJ  DRYBIO_AG
    ## 1      NA               NA 20.908857 20.908857 104.7298  6.018046 581.902600
    ## 2      NA               NA        NA        NA       NA 74.965282   2.410252
    ## 3      70                1  6.374361 18.212460       NA  6.018046 167.502609
    ## 4      NA                1  2.889440  2.889440       NA  6.018046  67.437885
    ## 5      NA               NA        NA        NA       NA 74.965282   3.925288
    ## 6      NA               NA  4.250839  4.250839       NA  6.018046 122.432583
    ##    CARBON_AG         BA TPROP_BASIS  tadjfac  DIACL2IN
    ## 1 290.951300 0.79852014        SUBP 1.006135 11.0-12.9
    ## 2   1.205126 0.01227150        MICR 1.006135   1.0-2.9
    ## 3  83.751305 0.78537600        SUBP 1.006135 11.0-12.9
    ## 4  33.718943 0.16498350        SUBP 1.006135   5.0-6.9
    ## 5   1.962644 0.01767096        MICR 1.006135   1.0-2.9
    ## 6  61.216292 0.25966494        SUBP 1.006135   5.0-6.9

    ## Look at table of new diameter classes (DIACL2IN)
    table(GBpopdat.bh$treex$DIACL2IN)

output

    ##
    ##   1.0-2.9   3.0-4.9   5.0-6.9   7.0-8.9  9.0-10.9 11.0-12.9 13.0-14.9 15.0-16.9
    ##        82        49       598       444       220       120        62        29
    ## 17.0-18.9 19.0-20.9 21.0-22.9 23.0-24.9 25.0-26.9 27.0-28.9 29.0-30.9 31.0-32.9
    ##        24        17         6         5         1         1         0         0
    ## 33.0-34.9 35.0-36.9 37.0-38.9 39.0-40.9 41.0-42.9 43.0-44.9 45.0-46.9 47.0-48.9
    ##         0         0         0         0         0         0         0         0
    ## 49.0-50.9 51.0-52.9 53.0-54.9 55.0-56.9 57.0-58.9 59.0-60.9 61.0-62.9 63.0-64.9
    ##         0         0         0         0         0         0         0         0
    ## 65.0-66.9 67.0-68.9 69.0-70.9 71.0-72.9 73.0-74.9 75.0-76.9 77.0-78.9 79.0-80.9
    ##         0         0         0         0         0         0         0         0

    ## Another way to append diameter classes
    ## First, create a new variable using cut function to define 4 diameter classes
    dat <- datLUTclass(x = GBpopdat.bh$treex,
                       xvar = "DIA",
                       cutbreaks = c(0, 5, 10, 20, 100))
    GBpopdat.bh$treex <- dat$xLUT

    ## Look at tree data, with new column (DIACL2IN)
    head(GBpopdat.bh$treex)

output

    ##           PLT_CN CONDID SUBP TREE STATUSCD SPCD SPGRPCD  DIA HT TREECLCD
    ## 1 40404876010690      1    1    9        1   19      12 12.1 64        2
    ## 2 40404876010690      1    1   15        1   19      12  1.5 11        2
    ## 3 40404876010690      1    2   18        2   19      12 12.0 57        3
    ## 4 40404876010690      1    2   22        2   19      12  5.5 53        3
    ## 5 40404876010690      1    2   26        1   19      12  1.8 12        2
    ## 6 40404876010690      1    4    3        1   19      12  6.9 46        2
    ##   AGENTCD STANDING_DEAD_CD  VOLCFNET  VOLCFGRS VOLBFNET TPA_UNADJ  DRYBIO_AG
    ## 1      NA               NA 20.908857 20.908857 104.7298  6.018046 581.902600
    ## 2      NA               NA        NA        NA       NA 74.965282   2.410252
    ## 3      70                1  6.374361 18.212460       NA  6.018046 167.502609
    ## 4      NA                1  2.889440  2.889440       NA  6.018046  67.437885
    ## 5      NA               NA        NA        NA       NA 74.965282   3.925288
    ## 6      NA               NA  4.250839  4.250839       NA  6.018046 122.432583
    ##    CARBON_AG         BA TPROP_BASIS  tadjfac  DIACL2IN   DIACL
    ## 1 290.951300 0.79852014        SUBP 1.006135 11.0-12.9 10-19.9
    ## 2   1.205126 0.01227150        MICR 1.006135   1.0-2.9   0-4.9
    ## 3  83.751305 0.78537600        SUBP 1.006135 11.0-12.9 10-19.9
    ## 4  33.718943 0.16498350        SUBP 1.006135   5.0-6.9   5-9.9
    ## 5   1.962644 0.01767096        MICR 1.006135   1.0-2.9   0-4.9
    ## 6  61.216292 0.25966494        SUBP 1.006135   5.0-6.9   5-9.9

    ## Look at table of new diameter classes (DIACL)
    table(GBpopdat.bh$treex$DIACL)

output

    ##
    ##   0-4.9   5-9.9 10-19.9     20+
    ##     131    1166     341      20

#### POP2: 2.3 Number of Live Trees by Species Groups and Diameter Class on forest land, Bighorn National Forest

View Example

We can also look at the number of live trees by species group and diameter class (DIACL2IN):

    ## Number of live trees by species group and diameter class (DIACL2IN)
    tree2.3 <- modGBtree(
        GBpopdat = GBpopdat.bh,      # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "TPA_UNADJ",               # est - number of trees per acre
        estvar.filter = "STATUSCD == 1",    # est - live trees only
        rowvar = "SPGRPCD",          # est - row domain
        colvar = "DIACL2IN",         # est - column domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = TRUE                # out - return output with est(pse)
          )
        )

Outputs:

    ## Look at output list
    names(tree2.3)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(tree2.3$est)

output

    ##                 Species group               1.0-2.9               3.0-4.9
    ## 1                 Douglas-fir 14,855,574.4 (100.76)  5,942,229.8 ( 60.41)
    ## 2                    True fir 35,729,466.5 ( 34.02) 23,768,919.1 ( 41.20)
    ## 3 Engelmann and other spruces 19,540,510.5 ( 52.06)  7,427,787.2 ( 59.10)
    ## 4              Lodgepole pine 43,081,165.8 ( 36.30) 34,281,953.1 ( 34.68)
    ## 5     Other western softwoods  2,971,114.9 (100.76)           -- (    --)
    ## 6        Cottonwood and aspen  5,942,229.8 (100.76)  1,485,557.4 (100.76)
    ##                 5.0-6.9               7.0-8.9              9.0-10.9
    ## 1  2,146,630.4 ( 46.77)  1,550,344.2 ( 60.21)    715,543.5 ( 56.70)
    ## 2  6,461,269.8 ( 32.39)  2,862,173.9 ( 36.06)    840,908.9 ( 36.35)
    ## 3  8,247,074.4 ( 29.96)  6,458,215.8 ( 34.84)  3,473,730.5 ( 36.37)
    ## 4 37,263,234.0 ( 24.24) 32,814,067.0 ( 21.39) 17,787,654.1 ( 20.44)
    ## 5  1,192,572.5 ( 82.60)    357,771.7 ( 74.35)    119,257.2 (100.76)
    ## 6  2,265,887.7 ( 54.81)    238,514.5 ( 70.35)    119,257.2 (100.76)
    ##              11.0-12.9            13.0-14.9            15.0-16.9
    ## 1   119,257.2 (100.76)   357,771.7 ( 74.35)   357,771.7 (100.76)
    ## 2 1,317,937.9 ( 44.43)   119,257.2 (100.76)   122,311.3 (101.17)
    ## 3 4,066,962.7 ( 28.16) 2,045,697.7 ( 39.54)   834,800.7 ( 64.86)
    ## 4 7,167,651.1 ( 22.35) 3,226,053.8 ( 30.61) 1,076,369.3 ( 41.49)
    ## 5          -- (    --)          -- (    --)          -- (    --)
    ## 6          -- (    --)          -- (    --)          -- (    --)
    ##            17.0-18.9            19.0-20.9          21.0-22.9          23.0-24.9
    ## 1        -- (    --)   119,257.2 (100.76) 119,257.2 (100.76) 119,257.2 (100.76)
    ## 2 119,257.2 (100.76)          -- (    --)        -- (    --)        -- (    --)
    ## 3 957,112.0 ( 55.17) 1,079,423.4 ( 42.09) 119,257.2 (100.76) 244,622.7 (101.17)
    ## 4 834,800.7 ( 74.03)   357,771.7 ( 74.35)        -- (    --) 119,257.2 (100.76)
    ## 5        -- (    --)          -- (    --)        -- (    --)        -- (    --)
    ## 6        -- (    --)          -- (    --)        -- (    --)        -- (    --)
    ##                    Total
    ## 1  26,402,894.7 ( 72.44)
    ## 2  71,341,501.9 ( 30.28)
    ## 3  54,495,194.9 ( 28.35)
    ## 4 178,009,977.8 ( 20.43)
    ## 5   4,640,716.3 ( 90.51)
    ## 6  10,051,446.6 ( 84.39)

#### POP2: 2.4 Number of Live Trees by Species Groups and a Different Diameter Class on forest land, Bighorn National Forest

View Example

Next, we can look at number of live trees by species group and diameter class (DIACL):

    ## Number of live trees by species group and diameter class (DIACL)
    tree2.4 <- modGBtree(
        GBpopdat = GBpopdat.bh,      # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "TPA_UNADJ",               # est - number of trees per acre
        estvar.filter = "STATUSCD == 1",    # est - live trees only
        rowvar = "SPGRPCD",          # est - row domain
        colvar = "DIACL",            # est - column domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = TRUE                # out - return output with est(pse)
          )
        )

Outputs:

    ## Look at output list
    names(tree2.4)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(tree2.4$est)

output

    ##                 Species group                 0-4.9                 5-9.9
    ## 1                 Douglas-fir 20,797,804.2 ( 86.59)  4,054,746.3 ( 51.25)
    ## 2                    True fir 59,498,385.6 ( 31.91) 10,045,095.4 ( 31.06)
    ## 3 Engelmann and other spruces 26,968,297.7 ( 45.56) 16,384,053.9 ( 32.40)
    ## 4              Lodgepole pine 77,363,118.9 ( 30.21) 80,587,209.0 ( 21.14)
    ## 5     Other western softwoods  2,971,114.9 (100.76)  1,550,344.2 ( 80.37)
    ## 6        Cottonwood and aspen  7,427,787.2 (100.76)  2,623,659.4 ( 56.07)
    ##                 10-19.9                20+                  Total
    ## 1  1,192,572.5 ( 47.37) 357,771.7 ( 74.35)  26,402,894.7 ( 72.44)
    ## 2  1,798,020.9 ( 38.16)        -- (    --)  71,341,501.9 ( 30.28)
    ## 3 10,415,083.4 ( 29.66) 727,759.8 ( 71.82)  54,495,194.9 ( 28.35)
    ## 4 19,821,135.4 ( 20.78) 238,514.5 ( 70.35) 178,009,977.8 ( 20.43)
    ## 5    119,257.2 (100.76)        -- (    --)   4,640,716.3 ( 90.51)
    ## 6           -- (    --)        -- (    --)  10,051,446.6 ( 84.39)

#### POP2: 2.5 Number of Live Trees (+ seedlings) by Species Groups and a Different Diameter Class on forest land, Bighorn National Forest

View Example

Finally, we add seedlings to Example 8:

    ## Number of live trees by species group and diameter class (DIACL), add seedlings
    tree2.5 <- modGBtree(
        GBpopdat = GBpopdat.bh,      # pop - population calculations
        estseed = "add",             # est - add seedling data
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "TPA_UNADJ",               # est - number of trees per acre
        estvar.filter = "STATUSCD == 1",    # est - live trees only
        rowvar = "SPGRPCD",          # est - row domain
        colvar = "DIACL",            # est - column domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = TRUE                # out - return output with est(pse)
          )
        )

And look at the outputs:

    ## Look at output list
    names(tree2.5)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(tree2.5$est)

output

    ##                 Species group                     <1                 0-4.9
    ## 1                 Douglas-fir  25,254,476.7 ( 89.00) 20,797,804.2 ( 86.59)
    ## 2                    True fir 339,352,003.3 ( 25.55) 59,498,385.6 ( 31.91)
    ## 3 Engelmann and other spruces  70,429,903.5 ( 39.28) 26,968,297.7 ( 45.56)
    ## 4              Lodgepole pine  72,868,402.9 ( 26.90) 77,363,118.9 ( 30.21)
    ## 5     Other western softwoods  34,167,821.3 ( 96.37)  2,971,114.9 (100.76)
    ## 6        Cottonwood and aspen  77,248,987.4 ( 84.02)  7,427,787.2 (100.76)
    ##                   5-9.9               10-19.9                20+
    ## 1  4,054,746.3 ( 51.25)  1,192,572.5 ( 47.37) 357,771.7 ( 74.35)
    ## 2 10,045,095.4 ( 31.06)  1,798,020.9 ( 38.16)        -- (    --)
    ## 3 16,384,053.9 ( 32.40) 10,415,083.4 ( 29.66) 727,759.8 ( 71.82)
    ## 4 80,587,209.0 ( 21.14) 19,821,135.4 ( 20.78) 238,514.5 ( 70.35)
    ## 5  1,550,344.2 ( 80.37)    119,257.2 (100.76)        -- (    --)
    ## 6  2,623,659.4 ( 56.07)           -- (    --)        -- (    --)
    ##                    Total
    ## 1  51,657,371.4 ( 80.14)
    ## 2 410,693,505.2 ( 24.79)
    ## 3 124,925,098.4 ( 31.82)
    ## 4 250,878,380.6 ( 19.12)
    ## 5  38,808,537.7 ( 95.62)
    ## 6  87,300,434.0 ( 76.24)

#### POP3: 3.1 Volume of Dead Trees by Forest Type Group and Primary Disturbance on forest land, Bighorn National Forest Districts

View Example

Next, we can look at number of live trees by species group and diameter class (DIACL):

    ## Number of dead trees by forest type group and primary disturbance
    tree3.1 <- modGBtree(
        GBpopdat = GBpopdat.bhdist,  # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = FALSE,            # est - sum estimation units to population
        estvar = "VOLCFNET",         # est - number of trees per acre
        estvar.filter = "STATUSCD == 2 & STANDING_DEAD_CD == 1",    # est - live trees only
        rowvar = "FORTYPGRPCD",      # est - row domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = TRUE                # out - return output with est(pse)
          )
        )

Outputs:

    ## Look at output list
    names(tree3.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    tree3.1$est

output

    ##                       Forest-type group Medicine Wheel Ranger District
    ## 1                     Douglas-fir group             165,168.4 ( 97.68)
    ## 2 Fir / spruce / mountain hemlock group         157,053,798.8 ( 48.22)
    ## 3                  Lodgepole pine group          36,412,218.0 ( 56.21)
    ## 4                 Aspen / birch / group                    -- (    --)
    ## 5                            Nonstocked                    -- (    --)
    ## 6                                 Total         193,631,185.1 ( 37.60)
    ##   Powder River Ranger District Tongue Ranger District                  Total
    ## 1                  -- (    --)            -- (    --)     130,081.0 (101.17)
    ## 2        71,872,642.4 ( 82.23)     467,834.6 (104.01) 218,544,932.5 ( 42.93)
    ## 3        12,310,437.3 ( 71.60)  31,018,741.5 ( 30.93)  77,694,274.8 ( 28.38)
    ## 4         7,703,877.9 ( 99.64)            -- (    --)   8,312,379.6 (101.17)
    ## 5                  -- (    --)   2,858,902.1 (104.01)   3,041,471.1 (101.17)
    ## 6        91,886,957.6 ( 63.23)  34,345,478.2 ( 28.07) 307,723,138.9 ( 30.19)

    ## Estimate and percent sampling error by district
    tree3.1$raw$unit_rowest

output

    ##                        DISTRICTNA                     Forest-type group
    ## 1  Medicine Wheel Ranger District                     Douglas-fir group
    ## 2  Medicine Wheel Ranger District Fir / spruce / mountain hemlock group
    ## 3  Medicine Wheel Ranger District                  Lodgepole pine group
    ## 4    Powder River Ranger District Fir / spruce / mountain hemlock group
    ## 5    Powder River Ranger District                  Lodgepole pine group
    ## 6    Powder River Ranger District                 Aspen / birch / group
    ## 7          Tongue Ranger District                     Douglas-fir group
    ## 8          Tongue Ranger District Fir / spruce / mountain hemlock group
    ## 9          Tongue Ranger District                  Lodgepole pine group
    ## 10         Tongue Ranger District                            Nonstocked
    ##           nhat     nhat.var NBRPLT.gt0 FORTYPGRPCD ACRES_GIS AREAUSED
    ## 1    0.4531147     0.195916          1         200  364517.8 364517.8
    ## 2  430.8535875 43165.341520          5         260  364517.8 364517.8
    ## 3   99.8914695  3152.227017          5         280  364517.8 364517.8
    ## 4  214.9748787 31249.654762          3         260  334330.4 334330.4
    ## 5   36.8211696   695.073514          3         280  334330.4 334330.4
    ## 6   23.0427067   527.155403          1         900  334330.4 334330.4
    ## 7    0.0000000     0.000000          0         200  413769.7 413769.7
    ## 8    1.1306641     1.383093          1         260  413769.7 413769.7
    ## 9   74.9661899   537.600991         13         280  413769.7 413769.7
    ## 10   6.9094033    51.649407          1         999  413769.7 413769.7
    ##            est      est.var     est.se    est.cv       pse CI99left   CI99right
    ## 1     165168.4 2.603200e+10   161344.3 0.9768476  97.68476        0    580763.9
    ## 2  157053798.8 5.735518e+15 75733202.6 0.4822118  48.22118        0 352129601.3
    ## 3   36412218.0 4.188466e+14 20465741.1 0.5620570  56.20570        0  89128473.6
    ## 4   71872642.4 3.492987e+15 59101501.1 0.8223087  82.23087        0 224108020.8
    ## 5   12310437.3 7.769312e+13  8814369.9 0.7160079  71.60079        0  35014749.4
    ## 6    7703877.9 5.892376e+13  7676181.4 0.9964049  99.64049        0  27476411.0
    ## 7          0.0 0.000000e+00        0.0       NaN       NaN        0         0.0
    ## 8     467834.6 2.367931e+11   486613.9 1.0401408 104.01408        0   1721268.9
    ## 9   31018741.5 9.204020e+13  9593758.1 0.3092891  30.92891  6306858  55730624.9
    ## 10   2858902.1 8.842658e+12  2973660.6 1.0401408 104.01408        0  10518544.3
    ##    CI95left   CI95right     CI68left   CI68right
    ## 1         0    481397.5     4718.238    325618.5
    ## 2   8619449 305488148.3 81740318.423 232367279.1
    ## 3         0  76524333.4 16059900.422  56764535.5
    ## 4         0 187709456.0 13098688.746 130646596.1
    ## 5         0  29586284.7  3544917.677  21075956.8
    ## 6         0  22748917.0    70238.786  15337517.0
    ## 7         0         0.0        0.000         0.0
    ## 8         0   1421580.3        0.000    951751.6
    ## 9  12215321  49822162.0 21478153.119  40559329.9
    ## 10        0   8687169.8        0.000   5816082.3

#### POP4: 4.1 Net cubic-foot volume of live trees by forest type group and stand-size class, Rhode Island, 2019

View Example

We can also look at the number of live trees by species group and diameter class (DIACL2IN):

    ## Net cubic-foot volume of live trees by forest type and stand-size class
    tree4.1 <- modGBtree(
        GBpopdat = GBpopdat.RI,      # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "VOLCFNET",         # est - net cubic-foot volume estimate
        estvar.filter = "STATUSCD == 1",    # est - live trees only
        rowvar = "FORTYPCD",         # est - row domain
        colvar = "STDSZCD",          # est - column domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          col.FIAname = TRUE,          # est - column domain names
          allin1 = TRUE                # out - return output with est(pse)
          )
        )

Outputs:

    ## Look at output list
    names(tree4.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(tree4.1$est)

output

    ##                                         Forest type         Large diameter
    ## 1                                Eastern white pine 111,564,015.5 ( 34.27)
    ## 2                                   Eastern hemlock   3,935,773.9 ( 95.01)
    ## 3                                        Pitch pine  29,414,660.9 ( 56.42)
    ## 4 Eastern white pine / northern red oak / white ash  44,820,940.9 ( 51.95)
    ## 5                             Other pine / hardwood   1,692,470.3 ( 94.75)
    ## 6                                      Chestnut oak            -- (    --)
    ##        Medium diameter Small diameter  Nonstocked                  Total
    ## 1          -- (    --)    -- (    --) -- (    --) 111,564,015.5 ( 34.27)
    ## 2          -- (    --)    -- (    --) -- (    --)   3,935,773.9 ( 95.01)
    ## 3          -- (    --)    -- (    --) -- (    --)  29,414,660.9 ( 56.42)
    ## 4 1,921,936.2 ( 69.09)    -- (    --) -- (    --)  46,742,877.1 ( 50.82)
    ## 5          -- (    --)    -- (    --) -- (    --)   1,692,470.3 ( 94.75)
    ## 6 5,958,482.8 ( 96.42)    -- (    --) -- (    --)   5,958,482.8 ( 96.42)

### modGBratio

`FIESTA`‘s `modGBratio` function generates per-acre and per-tree estimates by domain and/or tree domain by domain (e.g., Forest type) and/or tree domain (e.g., Species). Calculations are based on Scott et al. 2005 (’the green-book’) for mapped forest inventory plots. The ratio estimator for estimating per-acre or per-tree by stratum and domain is used, referred to as Ratio of Means (ROM).

If there are more than one estimation unit (i.e., subpopulation) within the population, estimates are generated by estimation unit. If sumunits = TRUE, the estimates and percent standard errors returned are a sum combination of all estimation units. If rawdata = TRUE, the raw data returned will include estimates by estimation unit.

Parameters defined in the following examples are organized by category: population data (pop); estimation information (est); and output details (out).

#### POP1: 1.1 Net cubic-foot volume per acre of live trees on timberland, Wyoming, 2011-2013

View Example

We generate estimates by estimation unit (i.e., ESTN_UNIT) and sum to population (i.e., WY):

    ## Return raw data and titles
    ## Total net cubic-foot volume of live trees (at least 5 inches diameter), Wyoming, 2011-2013
    ratio1.1 <- modGBratio(
        GBpopdat = GBpopdat,         # pop - population calculations
        landarea = "TIMBERLAND",     # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvarn = "VOLCFNET",               # est - net cubic-foot volume, numerator
        estvarn.filter = "STATUSCD == 1",   # est - live trees only, numerator
        returntitle = TRUE           # out - return title information
        )

And we can look at our output:

    ## Look at output list
    names(ratio1.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio1.1$est)

output

    ##   TOTAL Estimate Percent Sampling Error
    ## 1 Total     1749                   4.87

    ## Raw data (list object) for estimate
    raw1.1 <- ratio1.1$raw      # extract raw data list object from output
    names(raw1.1)

output

    ##  [1] "unit_totest"    "totest"         "domdat"         "estvarn"
    ##  [5] "estvarn.filter" "estvard"        "module"         "esttype"
    ##  [9] "GBmethod"       "rowvar"         "colvar"         "areaunits"
    ## [13] "estunitsn"

    head(raw1.1$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat  nhat.var       dhat     dhat.var     covar NBRPLT.gt0
    ## 1         1 191.27640  679.1225 0.16708433 3.208604e-04 0.2285831         18
    ## 2         3 220.30963 6020.2928 0.07623130 5.037465e-04 1.4161225          7
    ## 3         5  18.88212  116.3016 0.04111842 2.311568e-04 0.1066769          8
    ## 4         7 223.49294  763.3171 0.12439229 8.557688e-05 0.1455149         30
    ## 5         9  64.88298  579.3635 0.06862512 1.925392e-04 0.1447864         12
    ## 6        11 359.09031 4863.3286 0.24597749 6.412162e-04 0.7762385         25
    ##     ACRES AREAUSED       estn     estd     estn.var   estn.se   estn.cv
    ## 1 2757613  2757613  527466298 460753.9 5.164339e+15  71863335 0.1362425
    ## 2 2021729  2021729  445406358 154119.0 2.460727e+16 156867056 0.3521886
    ## 3 3072988  3072988   58024539 126356.4 1.098266e+15  33140094 0.5711393
    ## 4 5096959  5096959 1139134377 634022.4 1.983021e+16 140819770 0.1236200
    ## 5 2729653  2729653  177108024 187322.8 4.316841e+15  65702668 0.3709751
    ## 6 1837124  1837124  659693430 451891.1 1.641385e+16 128116563 0.1942062
    ##   estn.pse   estd.var  estd.se    estd.cv  estd.pse    est.covar      rhat
    ## 1 13.62425 2439960647 49395.96 0.10720681 10.720681 1.738244e+12 1144.7896
    ## 2 35.21886 2059007401 45376.29 0.29442363 29.442363 5.788242e+12 2890.0152
    ## 3 57.11393 2182872251 46721.22 0.36975739 36.975739 1.007377e+12  459.2132
    ## 4 12.36200 2223200929 47150.83 0.07436777  7.436777 3.780331e+12 1796.6784
    ## 5 37.09751 1434610660 37876.25 0.20219781 20.219781 1.078804e+12  945.4698
    ## 6 19.42062 2164120339 46520.11 0.10294538 10.294538 2.619824e+12 1459.8503
    ##    rhat.var  rhat.se   rhat.cv      pse  CI99left CI99right   CI95left
    ## 1  20641.95 143.6731 0.1255017 12.55017  774.7123 1514.8669  863.19555
    ## 2 351466.67 592.8462 0.2051360 20.51360 1362.9445 4417.0859 1728.05791
    ## 3  39670.77 199.1752 0.4337315 43.37315    0.0000  972.2546   68.83696
    ## 4  33391.19 182.7326 0.1017058 10.17058 1325.9905 2267.3663 1438.52913
    ## 5 101434.10 318.4872 0.3368560 33.68560  125.1011 1765.8385  321.24635
    ## 6  65506.75 255.9429 0.1753213 17.53213  800.5852 2119.1154  958.21152
    ##   CI95right  CI68left CI68right
    ## 1 1426.3836 1001.9128 1287.6664
    ## 2 4051.9725 2300.4546 3479.5758
    ## 3  849.5895  261.1419  657.2846
    ## 4 2154.8276 1614.9585 1978.3982
    ## 5 1569.6932  628.7477 1262.1919
    ## 6 1961.4891 1205.3259 1714.3747

    head(raw1.1$totest)      # estimates for population (i.e., WY)

output

    ##   TOTAL       estn     estn.var NBRPLT.gt0 AREAUSED    estd    estd.var
    ## 1     1 9772216510 3.785449e+17        281 54457532 5587398 54138597407
    ##      est.covar     rhat rhat.var  rhat.se    rhat.cv      pse CI99left
    ## 1 9.084184e+13 1748.974 7251.668 85.15673 0.04868952 4.868952 1529.625
    ##   CI99right CI95left CI95right CI68left CI68right
    ## 1  1968.324  1582.07  1915.879  1664.29  1833.659

    ## Titles (list object) for estimate
    titlelst <- ratio1.1$titlelst
    names(titlelst)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.yvard"
    ##  [5] "title.unitvar" "title.ref"     "outfn.estpse"  "outfn.rawdat"
    ##  [9] "outfn.param"   "title.tot"     "title.unitsn"  "title.unitsd"

    titlelst

output

    ## $title.estpse
    ## [1] "Net volume of live trees (at least 5 in dia) per acre, in cubic feet, and percent sampling error on timberland live"
    ##
    ## $title.yvar
    ## [1] "Net volume, in cubic feet"
    ##
    ## $title.estvar
    ## [1] "Net volume of live trees (at least 5 in dia) per acre"
    ##
    ## $title.yvard
    ## [1] "Acres"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "ratio_VOLCFNET_ACRE_live_timberland"
    ##
    ## $outfn.rawdat
    ## [1] "ratio_VOLCFNET_ACRE_live_timberland_rawdata"
    ##
    ## $outfn.param
    ## [1] "ratio_VOLCFNET_ACRE_live_timberland_parameters"
    ##
    ## $title.tot
    ## [1] "Net volume of live trees (at least 5 in dia) per acre, in cubic feet, on timberland live; Wyoming, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "cubic feet"
    ##
    ## $title.unitsd
    ## [1] "acres"

#### POP1: 1.2 Net cubic-foot volume per acre of live trees by forest type on timberland, Wyoming, 2011-2013

View Example

We can also add rows to the output:

    ## Net cubic-foot volume of live trees (at least 5 inches diameter) by forest type, Wyoming, 2011-2013
    ## Return raw data and titles
    ratio1.2 <- modGBratio(
        GBpopdat = GBpopdat,         # pop - population calculations
        landarea = "TIMBERLAND",     # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvarn = "VOLCFNET",               # est - net cubic-foot volume
        estvarn.filter = "STATUSCD == 1",   # est - live trees only
        rowvar = "FORTYPCD",         # est - row domain
        returntitle = TRUE           # out - return title information
        )

And of course view our outputs:

    ## Look at output list
    names(ratio1.2)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio1.2$est)

output

    ##   Forest type Estimate Percent Sampling Error
    ## 1         201   1742.2                  12.75
    ## 2         221   1347.4                  10.57
    ## 3         265   3239.5                  16.55
    ## 4         266   2701.4                   8.62
    ## 5         268   2102.4                  10.36
    ## 6         269   2904.4                      0

    ## Raw data (list object) for estimate
    raw1.2 <- ratio1.2$raw      # extract raw data list object from output
    names(raw1.2)

output

    ##  [1] "unit_totest"    "totest"         "unit_rowest"    "rowest"
    ##  [5] "domdat"         "estvarn"        "estvarn.filter" "estvard"
    ##  [9] "module"         "esttype"        "GBmethod"       "rowvar"
    ## [13] "colvar"         "areaunits"      "estunitsn"

    head(raw1.2$unit_totest)    # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat  nhat.var       dhat     dhat.var     covar NBRPLT.gt0
    ## 1         1 191.27640  679.1225 0.16708433 3.208604e-04 0.2285831         18
    ## 2         3 220.30963 6020.2928 0.07623130 5.037465e-04 1.4161225          7
    ## 3         5  18.88212  116.3016 0.04111842 2.311568e-04 0.1066769          8
    ## 4         7 223.49294  763.3171 0.12439229 8.557688e-05 0.1455149         30
    ## 5         9  64.88298  579.3635 0.06862512 1.925392e-04 0.1447864         12
    ## 6        11 359.09031 4863.3286 0.24597749 6.412162e-04 0.7762385         25
    ##     ACRES AREAUSED       estn     estd     estn.var   estn.se   estn.cv
    ## 1 2757613  2757613  527466298 460753.9 5.164339e+15  71863335 0.1362425
    ## 2 2021729  2021729  445406358 154119.0 2.460727e+16 156867056 0.3521886
    ## 3 3072988  3072988   58024539 126356.4 1.098266e+15  33140094 0.5711393
    ## 4 5096959  5096959 1139134377 634022.4 1.983021e+16 140819770 0.1236200
    ## 5 2729653  2729653  177108024 187322.8 4.316841e+15  65702668 0.3709751
    ## 6 1837124  1837124  659693430 451891.1 1.641385e+16 128116563 0.1942062
    ##   estn.pse   estd.var  estd.se    estd.cv  estd.pse    est.covar      rhat
    ## 1 13.62425 2439960647 49395.96 0.10720681 10.720681 1.738244e+12 1144.7896
    ## 2 35.21886 2059007401 45376.29 0.29442363 29.442363 5.788242e+12 2890.0152
    ## 3 57.11393 2182872251 46721.22 0.36975739 36.975739 1.007377e+12  459.2132
    ## 4 12.36200 2223200929 47150.83 0.07436777  7.436777 3.780331e+12 1796.6784
    ## 5 37.09751 1434610660 37876.25 0.20219781 20.219781 1.078804e+12  945.4698
    ## 6 19.42062 2164120339 46520.11 0.10294538 10.294538 2.619824e+12 1459.8503
    ##    rhat.var  rhat.se   rhat.cv      pse  CI99left CI99right   CI95left
    ## 1  20641.95 143.6731 0.1255017 12.55017  774.7123 1514.8669  863.19555
    ## 2 351466.67 592.8462 0.2051360 20.51360 1362.9445 4417.0859 1728.05791
    ## 3  39670.77 199.1752 0.4337315 43.37315    0.0000  972.2546   68.83696
    ## 4  33391.19 182.7326 0.1017058 10.17058 1325.9905 2267.3663 1438.52913
    ## 5 101434.10 318.4872 0.3368560 33.68560  125.1011 1765.8385  321.24635
    ## 6  65506.75 255.9429 0.1753213 17.53213  800.5852 2119.1154  958.21152
    ##   CI95right  CI68left CI68right
    ## 1 1426.3836 1001.9128 1287.6664
    ## 2 4051.9725 2300.4546 3479.5758
    ## 3  849.5895  261.1419  657.2846
    ## 4 2154.8276 1614.9585 1978.3982
    ## 5 1569.6932  628.7477 1262.1919
    ## 6 1961.4891 1205.3259 1714.3747

    head(raw1.2$totest)         # estimates for population (i.e., WY)

output

    ##   TOTAL       estn     estn.var NBRPLT.gt0 AREAUSED    estd    estd.var
    ## 1     1 9772216510 3.785449e+17        281 54457532 5587398 54138597407
    ##      est.covar     rhat rhat.var  rhat.se    rhat.cv      pse CI99left
    ## 1 9.084184e+13 1748.974 7251.668 85.15673 0.04868952 4.868952 1529.625
    ##   CI99right CI95left CI95right CI68left CI68right
    ## 1  1968.324  1582.07  1915.879  1664.29  1833.659

    head(raw1.2$unit_rowest)    # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT Forest type      nhat  nhat.var       dhat     dhat.var      covar
    ## 1         1         201 17.516131 237.14334 0.01023188 0.0000809180 0.13852497
    ## 2         1         221 34.018474 253.09891 0.03239380 0.0002137597 0.18126354
    ## 3         1         266 70.935057 823.57649 0.04092751 0.0002629835 0.45579984
    ## 4         1         268  9.434619  68.79919 0.01023188 0.0000809180 0.07461295
    ## 5         1         281 47.532459 511.47315 0.03854903 0.0002367898 0.31057313
    ## 6         1         901  9.455198  27.28348 0.02760699 0.0002055134 0.07021793
    ##   NBRPLT.gt0   ACRES AREAUSED      estn      estd     estn.var  estn.se
    ## 1          1 2757613  2757613  48302709  28215.56 1.803340e+15 42465749
    ## 2          4 2757613  2757613  93809786  89329.58 1.924673e+15 43871093
    ## 3          4 2757613  2757613 195611436 112862.22 6.262829e+15 79138040
    ## 4          1 2757613  2757613  26017027  28215.56 5.231785e+14 22873097
    ## 5          4 2757613  2757613 131076128 106303.32 3.889461e+15 62365547
    ## 6          3 2757613  2757613  26073778  76129.41 2.074753e+14 14404003
    ##     estn.cv estn.pse   estd.var  estd.se   estd.cv estd.pse    est.covar
    ## 1 0.8791587 87.91587  615335251 24805.95 0.8791587 87.91587 1.053403e+12
    ## 2 0.4676601 46.76601 1625520387 40317.74 0.4513370 45.13370 1.378406e+12
    ## 3 0.4045676 40.45676 1999839565 44719.57 0.3962315 39.62315 3.466098e+12
    ## 4 0.8791587 87.91587  615335251 24805.95 0.8791587 87.91587 5.673889e+11
    ## 5 0.4757964 47.57964 1800651626 42434.09 0.3991793 39.91793 2.361731e+12
    ## 6 0.5524325 55.24325 1562811900 39532.42 0.5192792 51.92792 5.339673e+11
    ##        rhat     rhat.var      rhat.se      rhat.cv          pse  CI99left
    ## 1 1711.9177 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1711.9177
    ## 2 1050.1537 1.030428e+05 3.210027e+02 3.056721e-01 3.056721e+01  223.3055
    ## 3 1733.1879 2.005284e+04 1.416081e+02 8.170381e-02 8.170381e+00 1368.4297
    ## 4  922.0810 1.570120e-10 1.253044e-05 1.358931e-08 1.358931e-06  922.0810
    ## 5 1233.0389 7.105340e+04 2.665584e+02 2.161801e-01 2.161801e+01  546.4299
    ## 6  342.4928 4.319640e+03 6.572397e+01 1.918988e-01 1.918988e+01  173.1991
    ##   CI99right  CI95left CI95right  CI68left CI68right
    ## 1 1711.9177 1711.9177 1711.9177 1711.9177 1711.9177
    ## 2 1877.0020  420.9999 1679.3075  730.9300 1369.3774
    ## 3 2097.9461 1455.6412 2010.7346 1592.3646 1874.0111
    ## 4  922.0810  922.0810  922.0810  922.0810  922.0810
    ## 5 1919.6479  710.5940 1755.4838  967.9578 1498.1200
    ## 6  511.7866  213.6762  471.3095  277.1331  407.8526

    head(raw1.2$rowest)         # estimates by row for population (i.e., WY)

output

    ##   Forest type       estn     estn.var NBRPLT.gt0 AREAUSED      estd    estd.var
    ## 1         201 1152902217 6.003016e+16         36 27936078 661754.69 12118625799
    ## 2         221 1198569953 3.486594e+16         51 29512832 889542.77 13008784420
    ## 3         265  902842868 8.210445e+16         16 26280468 278701.09  5375334483
    ## 4         266 2433738758 1.564436e+17         45 30361235 900929.05 15512198557
    ## 5         268 1309551697 6.532851e+16         33 36531333 622891.26 10860191917
    ## 6         269   55531261 3.207455e+15          1  2616954  19119.96   380241334
    ##      est.covar     rhat  rhat.var  rhat.se    rhat.cv       pse  CI99left
    ## 1 2.158061e+13 1742.190  49364.98 222.1823 0.12753049 12.753049 1169.8860
    ## 2 1.574556e+13 1347.400  20286.06 142.4291 0.10570664 10.570664  980.5269
    ## 3 1.793238e+13 3239.467 287498.90 536.1892 0.16551775 16.551775 1858.3347
    ## 4 4.176763e+13 2701.366  54187.66 232.7824 0.08617213  8.617213 2101.7578
    ## 5 2.257173e+13 2102.376  47479.86 217.8987 0.10364403 10.364403 1541.1061
    ## 6 1.104358e+12 2904.361      0.00   0.0000 0.00000000  0.000000 2904.3611
    ##   CI99right CI95left CI95right CI68left CI68right
    ## 1  2314.493 1306.720  2177.659 1521.239  1963.141
    ## 2  1714.273 1068.244  1626.556 1205.760  1489.040
    ## 3  4620.599 2188.555  4290.378 2706.249  3772.684
    ## 4  3300.973 2245.120  3157.611 2469.873  2932.858
    ## 5  2663.646 1675.302  2529.450 1885.685  2319.067
    ## 6  2904.361 2904.361  2904.361 2904.361  2904.361

    ## Titles (list object) for estimate
    titlelst <- ratio1.2$titlelst
    names(titlelst)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.yvard"
    ##  [5] "title.unitvar" "title.ref"     "outfn.estpse"  "outfn.rawdat"
    ##  [9] "outfn.param"   "title.rowvar"  "title.row"     "title.unitsn"
    ## [13] "title.unitsd"

    titlelst

output

    ## $title.estpse
    ## [1] "Net volume of live trees (at least 5 in dia) per acre, in cubic feet, and percent sampling error on timberland by forest type live"
    ##
    ## $title.yvar
    ## [1] "Net volume, in cubic feet"
    ##
    ## $title.estvar
    ## [1] "Net volume of live trees (at least 5 in dia) per acre"
    ##
    ## $title.yvard
    ## [1] "Acres"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "ratio_VOLCFNET_ACRE_live_FORTYPCD_timberland"
    ##
    ## $outfn.rawdat
    ## [1] "ratio_VOLCFNET_ACRE_live_FORTYPCD_timberland_rawdata"
    ##
    ## $outfn.param
    ## [1] "ratio_VOLCFNET_ACRE_live_FORTYPCD_timberland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Net volume of live trees (at least 5 in dia) per acre, in cubic feet, on timberland by forest type live; Wyoming, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "cubic feet"
    ##
    ## $title.unitsd
    ## [1] "acres"

#### POP1: 1.3 Net cubic-foot volume per acre of live trees by forest type and stand-size class on timberland, Wyoming, 2011-2013

View Example

We can also add row and columns to output, including FIA names:

    ## Return output with estimates (est) and percent standard error (pse) in same cell - est(pse)
    ## Save data to outfolder:
    ## Net cubic-foot volume of live trees (at least 5 inches diameter) by forest type and stand-size class,
    ##    Wyoming, 2011-2013
    ratio1.3 <- modGBratio(
        GBpopdat = GBpopdat,         # pop - population calculations
        landarea = "TIMBERLAND",     # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvarn = "VOLCFNET",               # est - net cubic-foot volume, numerator
        estvarn.filter = "STATUSCD == 1",   # est - live trees only, numerator
        rowvar = "FORTYPCD",         # est - row domain
        colvar = "STDSZCD",          # est - column domain
        returntitle = TRUE,          # out - return title information
        savedata = TRUE,             # out - save data to outfolder
        table_opts = list(
          row.FIAname = TRUE,          # est - row domain names
          col.FIAname = TRUE,          # est - column domain names
          allin1 = TRUE                # out - return output with est(pse)
          ),
        savedata_opts = list(
          outfolder = outfolder,       # out - outfolder for saving data
          outfn.pre = "WY"             # out - prefix for output files
          )
        )

And look at our output again:

    ## Look at output list from modGBarea()
    names(ratio1.3)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio1.3$est)

output

    ##                        Forest type  Large diameter Medium diameter
    ## 1                      Douglas-fir 2,257.2 (11.37)   937.3 (21.47)
    ## 2                   Ponderosa pine 1,448.4 (10.39) 1,132.3 (47.22)
    ## 3                 Engelmann spruce 3,659.0 (17.91) 2,687.6 (24.21)
    ## 4 Engelmann spruce / subalpine fir 3,282.9 ( 9.11) 1,969.2 ( 8.46)
    ## 5                    Subalpine fir 2,598.4 ( 8.77) 1,579.1 (24.49)
    ## 6                      Blue spruce 2,904.4 (   --)      -- (   --)
    ##   Small diameter Nonstocked           Total
    ## 1  314.9 (60.45) -- (   --) 1,742.2 (12.75)
    ## 2  109.1 (59.26) -- (   --) 1,347.4 (10.57)
    ## 3  815.9 (26.30) -- (   --) 3,239.5 (16.55)
    ## 4  961.9 (24.80) -- (   --) 2,701.4 ( 8.62)
    ## 5  444.3 (30.76) -- (   --) 2,102.4 (10.36)
    ## 6     -- (   --) -- (   --) 2,904.4 (   --)

    ## Raw data (list object) for estimate
    raw1.3 <- ratio1.3$raw      # extract raw data list object from output
    names(raw1.3)

output

    ##  [1] "unit_totest"    "totest"         "unit_rowest"    "rowest"
    ##  [5] "unit_colest"    "colest"         "unit_grpest"    "grpest"
    ##  [9] "domdat"         "estvarn"        "estvarn.filter" "estvard"
    ## [13] "module"         "esttype"        "GBmethod"       "rowvar"
    ## [17] "colvar"         "areaunits"      "estunitsn"

    head(raw1.3$unit_totest) # estimates by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT      nhat  nhat.var       dhat     dhat.var     covar NBRPLT.gt0
    ## 1         1 191.27640  679.1225 0.16708433 3.208604e-04 0.2285831         18
    ## 2         3 220.30963 6020.2928 0.07623130 5.037465e-04 1.4161225          7
    ## 3         5  18.88212  116.3016 0.04111842 2.311568e-04 0.1066769          8
    ## 4         7 223.49294  763.3171 0.12439229 8.557688e-05 0.1455149         30
    ## 5         9  64.88298  579.3635 0.06862512 1.925392e-04 0.1447864         12
    ## 6        11 359.09031 4863.3286 0.24597749 6.412162e-04 0.7762385         25
    ##     ACRES AREAUSED       estn     estd     estn.var   estn.se   estn.cv
    ## 1 2757613  2757613  527466298 460753.9 5.164339e+15  71863335 0.1362425
    ## 2 2021729  2021729  445406358 154119.0 2.460727e+16 156867056 0.3521886
    ## 3 3072988  3072988   58024539 126356.4 1.098266e+15  33140094 0.5711393
    ## 4 5096959  5096959 1139134377 634022.4 1.983021e+16 140819770 0.1236200
    ## 5 2729653  2729653  177108024 187322.8 4.316841e+15  65702668 0.3709751
    ## 6 1837124  1837124  659693430 451891.1 1.641385e+16 128116563 0.1942062
    ##   estn.pse   estd.var  estd.se    estd.cv  estd.pse    est.covar      rhat
    ## 1 13.62425 2439960647 49395.96 0.10720681 10.720681 1.738244e+12 1144.7896
    ## 2 35.21886 2059007401 45376.29 0.29442363 29.442363 5.788242e+12 2890.0152
    ## 3 57.11393 2182872251 46721.22 0.36975739 36.975739 1.007377e+12  459.2132
    ## 4 12.36200 2223200929 47150.83 0.07436777  7.436777 3.780331e+12 1796.6784
    ## 5 37.09751 1434610660 37876.25 0.20219781 20.219781 1.078804e+12  945.4698
    ## 6 19.42062 2164120339 46520.11 0.10294538 10.294538 2.619824e+12 1459.8503
    ##    rhat.var  rhat.se   rhat.cv      pse  CI99left CI99right   CI95left
    ## 1  20641.95 143.6731 0.1255017 12.55017  774.7123 1514.8669  863.19555
    ## 2 351466.67 592.8462 0.2051360 20.51360 1362.9445 4417.0859 1728.05791
    ## 3  39670.77 199.1752 0.4337315 43.37315    0.0000  972.2546   68.83696
    ## 4  33391.19 182.7326 0.1017058 10.17058 1325.9905 2267.3663 1438.52913
    ## 5 101434.10 318.4872 0.3368560 33.68560  125.1011 1765.8385  321.24635
    ## 6  65506.75 255.9429 0.1753213 17.53213  800.5852 2119.1154  958.21152
    ##   CI95right  CI68left CI68right
    ## 1 1426.3836 1001.9128 1287.6664
    ## 2 4051.9725 2300.4546 3479.5758
    ## 3  849.5895  261.1419  657.2846
    ## 4 2154.8276 1614.9585 1978.3982
    ## 5 1569.6932  628.7477 1262.1919
    ## 6 1961.4891 1205.3259 1714.3747

    head(raw1.3$totest)      # estimates for population (i.e., WY)

output

    ##   TOTAL       estn     estn.var NBRPLT.gt0 AREAUSED    estd    estd.var
    ## 1     1 9772216510 3.785449e+17        281 54457532 5587398 54138597407
    ##      est.covar     rhat rhat.var  rhat.se    rhat.cv      pse CI99left
    ## 1 9.084184e+13 1748.974 7251.668 85.15673 0.04868952 4.868952 1529.625
    ##   CI99right CI95left CI95right CI68left CI68right
    ## 1  1968.324  1582.07  1915.879  1664.29  1833.659

    head(raw1.3$unit_rowest) # estimates by row, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT                      Forest type      nhat  nhat.var       dhat
    ## 1         1                      Douglas-fir 17.516131 237.14334 0.01023188
    ## 2         1                   Ponderosa pine 34.018474 253.09891 0.03239380
    ## 3         1 Engelmann spruce / subalpine fir 70.935057 823.57649 0.04092751
    ## 4         1                    Subalpine fir  9.434619  68.79919 0.01023188
    ## 5         1                   Lodgepole pine 47.532459 511.47315 0.03854903
    ## 6         1                            Aspen  9.455198  27.28348 0.02760699
    ##       dhat.var      covar NBRPLT.gt0 FORTYPCD   ACRES AREAUSED      estn
    ## 1 0.0000809180 0.13852497          1      201 2757613  2757613  48302709
    ## 2 0.0002137597 0.18126354          4      221 2757613  2757613  93809786
    ## 3 0.0002629835 0.45579984          4      266 2757613  2757613 195611436
    ## 4 0.0000809180 0.07461295          1      268 2757613  2757613  26017027
    ## 5 0.0002367898 0.31057313          4      281 2757613  2757613 131076128
    ## 6 0.0002055134 0.07021793          3      901 2757613  2757613  26073778
    ##        estd     estn.var  estn.se   estn.cv estn.pse   estd.var  estd.se
    ## 1  28215.56 1.803340e+15 42465749 0.8791587 87.91587  615335251 24805.95
    ## 2  89329.58 1.924673e+15 43871093 0.4676601 46.76601 1625520387 40317.74
    ## 3 112862.22 6.262829e+15 79138040 0.4045676 40.45676 1999839565 44719.57
    ## 4  28215.56 5.231785e+14 22873097 0.8791587 87.91587  615335251 24805.95
    ## 5 106303.32 3.889461e+15 62365547 0.4757964 47.57964 1800651626 42434.09
    ## 6  76129.41 2.074753e+14 14404003 0.5524325 55.24325 1562811900 39532.42
    ##     estd.cv estd.pse    est.covar      rhat     rhat.var      rhat.se
    ## 1 0.8791587 87.91587 1.053403e+12 1711.9177 0.000000e+00 0.000000e+00
    ## 2 0.4513370 45.13370 1.378406e+12 1050.1537 1.030428e+05 3.210027e+02
    ## 3 0.3962315 39.62315 3.466098e+12 1733.1879 2.005284e+04 1.416081e+02
    ## 4 0.8791587 87.91587 5.673889e+11  922.0810 1.570120e-10 1.253044e-05
    ## 5 0.3991793 39.91793 2.361731e+12 1233.0389 7.105340e+04 2.665584e+02
    ## 6 0.5192792 51.92792 5.339673e+11  342.4928 4.319640e+03 6.572397e+01
    ##        rhat.cv          pse  CI99left CI99right  CI95left CI95right  CI68left
    ## 1 0.000000e+00 0.000000e+00 1711.9177 1711.9177 1711.9177 1711.9177 1711.9177
    ## 2 3.056721e-01 3.056721e+01  223.3055 1877.0020  420.9999 1679.3075  730.9300
    ## 3 8.170381e-02 8.170381e+00 1368.4297 2097.9461 1455.6412 2010.7346 1592.3646
    ## 4 1.358931e-08 1.358931e-06  922.0810  922.0810  922.0810  922.0810  922.0810
    ## 5 2.161801e-01 2.161801e+01  546.4299 1919.6479  710.5940 1755.4838  967.9578
    ## 6 1.918988e-01 1.918988e+01  173.1991  511.7866  213.6762  471.3095  277.1331
    ##   CI68right
    ## 1 1711.9177
    ## 2 1369.3774
    ## 3 1874.0111
    ## 4  922.0810
    ## 5 1498.1200
    ## 6  407.8526

    head(raw1.3$rowest)      # estimates by row for population (i.e., WY)

output

    ##                        Forest type FORTYPCD       estn     estn.var NBRPLT.gt0
    ## 1                      Douglas-fir      201 1152902217 6.003016e+16         36
    ## 2                   Ponderosa pine      221 1198569953 3.486594e+16         51
    ## 3                 Engelmann spruce      265  902842868 8.210445e+16         16
    ## 4 Engelmann spruce / subalpine fir      266 2433738758 1.564436e+17         45
    ## 5                    Subalpine fir      268 1309551697 6.532851e+16         33
    ## 6                      Blue spruce      269   55531261 3.207455e+15          1
    ##   AREAUSED      estd    estd.var    est.covar     rhat  rhat.var  rhat.se
    ## 1 27936078 661754.69 12118625799 2.158061e+13 1742.190  49364.98 222.1823
    ## 2 29512832 889542.77 13008784420 1.574556e+13 1347.400  20286.06 142.4291
    ## 3 26280468 278701.09  5375334483 1.793238e+13 3239.467 287498.90 536.1892
    ## 4 30361235 900929.05 15512198557 4.176763e+13 2701.366  54187.66 232.7824
    ## 5 36531333 622891.26 10860191917 2.257173e+13 2102.376  47479.86 217.8987
    ## 6  2616954  19119.96   380241334 1.104358e+12 2904.361      0.00   0.0000
    ##      rhat.cv       pse  CI99left CI99right CI95left CI95right CI68left
    ## 1 0.12753049 12.753049 1169.8860  2314.493 1306.720  2177.659 1521.239
    ## 2 0.10570664 10.570664  980.5269  1714.273 1068.244  1626.556 1205.760
    ## 3 0.16551775 16.551775 1858.3347  4620.599 2188.555  4290.378 2706.249
    ## 4 0.08617213  8.617213 2101.7578  3300.973 2245.120  3157.611 2469.873
    ## 5 0.10364403 10.364403 1541.1061  2663.646 1675.302  2529.450 1885.685
    ## 6 0.00000000  0.000000 2904.3611  2904.361 2904.361  2904.361 2904.361
    ##   CI68right
    ## 1  1963.141
    ## 2  1489.040
    ## 3  3772.684
    ## 4  2932.858
    ## 5  2319.067
    ## 6  2904.361

    head(raw1.3$unit_colest) # estimates by column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT Stand-size class       nhat    nhat.var        dhat     dhat.var
    ## 1         1   Large diameter  92.576964  854.213271 0.063089433 3.512849e-04
    ## 2         1  Medium diameter  86.699781  700.129293 0.061391258 3.337868e-04
    ## 3         1   Small diameter   9.615193   27.172483 0.035460400 2.376570e-04
    ## 4         1       Nonstocked   2.384466    5.993907 0.007143242 5.379211e-05
    ## 5         3   Large diameter 213.616471 5982.044991 0.058381005 4.307422e-04
    ## 6         3  Medium diameter   6.693154   44.810694 0.007729145 5.975619e-05
    ##        covar NBRPLT.gt0 STDSZCD   ACRES AREAUSED      estn      estd
    ## 1 0.46727363          7       1 2757613  2757613 255291439 173976.24
    ## 2 0.47139023          6       2 2757613  2757613 239084444 169293.33
    ## 3 0.06782116          4       3 2757613  2757613  26514982  97786.06
    ## 4 0.01795619          1       5 2757613  2757613   6575434  19698.30
    ## 5 1.54026382          6       1 2021729  2021729 431874614 118030.57
    ## 6 0.05174666          1       2 2021729  2021729  13531744  15626.24
    ##       estn.var   estn.se   estn.cv  estn.pse   estd.var  estd.se   estd.cv
    ## 1 6.495805e+15  80596554 0.3157041  31.57041 2671321181 51684.83 0.2970798
    ## 2 5.324084e+15  72966320 0.3051906  30.51906 2538257910 50381.13 0.2975966
    ## 3 2.066312e+14  14374673 0.5421340  54.21340 1807245526 42511.71 0.4347420
    ## 4 4.558025e+13   6751314 1.0267481 102.67481  409058305 20225.19 1.0267481
    ## 5 2.445094e+16 156367963 0.3620680  36.20680 1760610740 41959.63 0.3554980
    ## 6 1.831587e+14  13533614 1.0001382 100.01382  244246754 15628.40 1.0001382
    ##    estd.pse    est.covar      rhat     rhat.var      rhat.se      rhat.cv
    ## 1  29.70798 3.553349e+12 1467.3925 6.011305e+04 2.451796e+02 1.670852e-01
    ## 2  29.75966 3.584654e+12 1412.2496 9.129651e+03 9.554921e+01 6.765745e-02
    ## 3  43.47420 5.157412e+11  271.1530 6.255676e+03 7.909283e+01 2.916908e-01
    ## 4 102.67481 1.365466e+11  333.8072 0.000000e+00 0.000000e+00 0.000000e+00
    ## 5  35.54980 6.295656e+12 3659.0064 1.400367e+05 3.742148e+02 1.022722e-01
    ## 6 100.01382 2.115087e+11  865.9631 5.119190e-10 2.262563e-05 2.612770e-08
    ##            pse   CI99left CI99right  CI95left CI95right  CI68left CI68right
    ## 1 1.670852e+01  835.85166 2098.9334  986.8493 1947.9358 1223.5717 1711.2134
    ## 2 6.765745e+00 1166.13118 1658.3681 1224.9766 1599.5226 1317.2300 1507.2693
    ## 3 2.916908e+01   67.42334  474.8826  116.1339  426.1721  192.4985  349.8075
    ## 4 0.000000e+00  333.80722  333.8072  333.8072  333.8072  333.8072  333.8072
    ## 5 1.022722e+01 2695.09296 4622.9198 2925.5589 4392.4539 3286.8655 4031.1473
    ## 6 2.612770e-06  865.96302  865.9631  865.9630  865.9631  865.9631  865.9631

    head(raw1.3$colest)      # estimates by column for population (i.e., WY)

output

    ##   Stand-size class STDSZCD       estn     estn.var NBRPLT.gt0 AREAUSED
    ## 1   Large diameter       1 7064786085 3.550113e+17        161 50335177
    ## 2  Medium diameter       2 2226735208 8.660019e+16         71 43191207
    ## 3   Small diameter       3  446586704 7.657716e+15         51 44018532
    ## 4       Nonstocked       5   34108513 2.870730e+14         11 36291808
    ##        estd    estd.var    est.covar      rhat  rhat.var   rhat.se    rhat.cv
    ## 1 2857890.7 38575741797 9.464419e+13 2472.0281 15037.387 122.62702 0.04960584
    ## 2 1345938.0 22537912872 3.785889e+13 1654.4115 12707.271 112.72653 0.06813694
    ## 3 1095815.5 19643622069 8.162301e+12  407.5382  3553.742  59.61327 0.14627651
    ## 4  287753.9  5235434732 6.469952e+11  118.5336  2502.957  50.02956 0.42207065
    ##         pse  CI99left CI99right   CI95left CI95right   CI68left CI68right
    ## 1  4.960584 2156.1618 2787.8944 2231.68355 2712.3727 2350.08069 2593.9755
    ## 2  6.813694 1364.0472 1944.7758 1433.47153 1875.3514 1542.30968 1766.5133
    ## 3 14.627651  253.9846  561.0918  290.69835  524.3781  348.25532  466.8211
    ## 4 42.207065    0.0000  247.4012   20.47748  216.5897   68.78132  168.2859

    head(raw1.3$unit_grpest) # estimates by row and column, by estimation unit (i.e., ESTN_UNIT)

output

    ##   ESTN_UNIT                      Forest type Stand-size class      nhat
    ## 1         1                      Douglas-fir   Large diameter 17.516131
    ## 2         1                   Ponderosa pine   Large diameter 34.018474
    ## 3         1 Engelmann spruce / subalpine fir   Large diameter 41.042359
    ## 4         1 Engelmann spruce / subalpine fir  Medium diameter 29.892698
    ## 5         1                    Subalpine fir  Medium diameter  9.434619
    ## 6         1                   Lodgepole pine  Medium diameter 47.372464
    ##    nhat.var       dhat     dhat.var      covar NBRPLT.gt0 STDSZCD FORTYPCD
    ## 1 237.14334 0.01023188 0.0000809180 0.13852497          1       1      201
    ## 2 253.09891 0.03239380 0.0002137597 0.18126354          4       1      221
    ## 3 615.72096 0.02046375 0.0001517213 0.30429405          2       1      266
    ## 4 326.38928 0.02046375 0.0001517213 0.22162884          2       2      266
    ## 5  68.79919 0.01023188 0.0000809180 0.07461295          1       2      268
    ## 6 512.18564 0.03069563 0.0002124098 0.32781129          3       2      281
    ##     ACRES AREAUSED      estn     estd     estn.var  estn.se   estn.cv estn.pse
    ## 1 2757613  2757613  48302709 28215.56 1.803340e+15 42465749 0.8791587 87.91587
    ## 2 2757613  2757613  93809786 89329.58 1.924673e+15 43871093 0.4676601 46.76601
    ## 3 2757613  2757613 113178943 56431.11 4.682207e+15 68426651 0.6045882 60.45882
    ## 4 2757613  2757613  82432493 56431.11 2.482004e+15 49819718 0.6043699 60.43699
    ## 5 2757613  2757613  26017027 28215.56 5.231785e+14 22873097 0.8791587 87.91587
    ## 6 2757613  2757613 130634924 84646.67 3.894880e+15 62408970 0.4777357 47.77357
    ##     estd.var  estd.se   estd.cv estd.pse    est.covar     rhat     rhat.var
    ## 1  615335251 24805.95 0.8791587 87.91587 1.053403e+12 1711.918 0.000000e+00
    ## 2 1625520387 40317.74 0.4513370 45.13370 1.378406e+12 1050.154 1.030428e+05
    ## 3 1153753595 33966.95 0.6019188 60.19188 2.313983e+12 2005.613 1.295474e+04
    ## 4 1153753595 33966.95 0.6019188 60.19188 1.685361e+12 1460.763 6.309071e+03
    ## 5  615335251 24805.95 0.8791587 87.91587 5.673889e+11  922.081 1.570120e-10
    ## 6 1615255033 40190.24 0.4748000 47.48000 2.492818e+12 1543.297 6.660371e+03
    ##        rhat.se      rhat.cv          pse  CI99left CI99right  CI95left
    ## 1 0.000000e+00 0.000000e+00 0.000000e+00 1711.9177  1711.918 1711.9177
    ## 2 3.210027e+02 3.056721e-01 3.056721e+01  223.3055  1877.002  420.9999
    ## 3 1.138189e+02 5.675019e-02 5.675019e+00 1712.4345  2298.791 1782.5316
    ## 4 7.942966e+01 5.437545e-02 5.437545e+00 1256.1660  1665.360 1305.0840
    ## 5 1.253044e-05 1.358931e-08 1.358931e-06  922.0810   922.081  922.0810
    ## 6 8.161110e+01 5.288101e-02 5.288101e+00 1333.0805  1753.513 1383.3419
    ##   CI95right CI68left CI68right
    ## 1  1711.918 1711.918  1711.918
    ## 2  1679.308  730.930  1369.377
    ## 3  2228.693 1892.424  2118.801
    ## 4  1616.443 1381.774  1539.753
    ## 5   922.081  922.081   922.081
    ## 6  1703.252 1462.138  1624.456

    head(raw1.3$grpest)      # estimates by row and column for population (i.e., WY)

output

    ##      Forest type Stand-size class FORTYPCD STDSZCD       estn     estn.var
    ## 1    Douglas-fir   Large diameter      201       1 1016954545 5.734409e+16
    ## 2    Douglas-fir  Medium diameter      201       2  104576458 2.341819e+15
    ## 3    Douglas-fir   Small diameter      201       3   31371213 5.714050e+14
    ## 4 Ponderosa pine   Large diameter      221       1 1139547784 3.501597e+16
    ## 5 Ponderosa pine  Medium diameter      221       2   52902315 1.339518e+15
    ## 6 Ponderosa pine   Small diameter      221       3    6119854 2.791269e+13
    ##   NBRPLT.gt0 AREAUSED      estd    estd.var    est.covar      rhat   rhat.var
    ## 1         26 25914349 450541.00  8124659016 1.890982e+13 2257.1854  65877.821
    ## 2          6 15113584 111576.95  2129966306 1.978525e+12  937.2586  40493.137
    ## 3          6 11396039  99636.75  1792776867 6.184506e+11  314.8559  36231.237
    ## 4         46 28161863 786751.82 11316613869 1.543968e+13 1448.4209  22668.087
    ## 5          3  3373162  46720.27   799177795 7.683578e+11 1132.3205 285932.640
    ## 6          3  7864402  56070.68  1071721490 1.261115e+11  109.1454   4182.934
    ##    rhat.se   rhat.cv      pse  CI99left CI99right   CI95left CI95right
    ## 1 256.6668 0.1137110 11.37110 1596.0556 2918.3151 1754.12778 2760.2430
    ## 2 201.2291 0.2146996 21.46996  418.9269 1455.5904  542.85691 1331.6604
    ## 3 190.3450 0.6045466 60.45466    0.0000  805.1522    0.00000  687.9253
    ## 4 150.5592 0.1039472 10.39472 1060.6060 1836.2358 1153.33020 1743.5116
    ## 5 534.7267 0.4722397 47.22397    0.0000 2509.6852   84.27542 2180.3655
    ## 6  64.6756 0.5925640 59.25640    0.0000  275.7387    0.00000  235.9072
    ##     CI68left CI68right
    ## 1 2001.94109 2512.4296
    ## 2  737.14480 1137.3725
    ## 3  125.56572  504.1460
    ## 4 1298.69607 1598.1457
    ## 5  600.55730 1664.0837
    ## 6   44.82819  173.4625

    ## Titles (list object) for estimate
    titlelst <- ratio1.3$titlelst
    names(titlelst)

output

    ##  [1] "title.estpse"  "title.yvar"    "title.estvar"  "title.yvard"
    ##  [5] "title.unitvar" "title.ref"     "outfn.estpse"  "outfn.rawdat"
    ##  [9] "outfn.param"   "title.rowvar"  "title.row"     "title.colvar"
    ## [13] "title.col"     "title.unitsn"  "title.unitsd"

    titlelst

output

    ## $title.estpse
    ## [1] "Net volume of live trees (at least 5 in dia) per acre, in cubic feet (percent sampling error), by forest type and stand-size class on timberland live"
    ##
    ## $title.yvar
    ## [1] "Net volume, in cubic feet"
    ##
    ## $title.estvar
    ## [1] "Net volume of live trees (at least 5 in dia) per acre"
    ##
    ## $title.yvard
    ## [1] "Acres"
    ##
    ## $title.unitvar
    ## [1] "ESTN_UNIT"
    ##
    ## $title.ref
    ## [1] "Wyoming, 2011-2013"
    ##
    ## $outfn.estpse
    ## [1] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland"
    ##
    ## $outfn.rawdat
    ## [1] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata"
    ##
    ## $outfn.param
    ## [1] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Forest type"
    ##
    ## $title.row
    ## [1] "Net volume of live trees (at least 5 in dia) per acre, in cubic feet (percent sampling error), by forest type on timberland live; Wyoming, 2011-2013"
    ##
    ## $title.colvar
    ## [1] "Stand-size class"
    ##
    ## $title.col
    ## [1] "Net volume of live trees (at least 5 in dia) per acre, in cubic feet (percent sampling error), by stand-size class on timberland live; Wyoming, 2011-2013"
    ##
    ## $title.unitsn
    ## [1] "cubic feet"
    ##
    ## $title.unitsd
    ## [1] "acres"

    ## List output files in outfolder
    list.files(outfolder, pattern = "WY_ratio")

output

    ## [1] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland.csv"

    list.files(paste0(outfolder, "/rawdata"), pattern = "WY_ratio")

output

    ## [1] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata_colest.csv"
    ## [2] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata_domdat.csv"
    ## [3] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata_grpest.csv"
    ## [4] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata_rowest.csv"
    ## [5] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata_totest.csv"
    ## [6] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata_unit_colest.csv"
    ## [7] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata_unit_grpest.csv"
    ## [8] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata_unit_rowest.csv"
    ## [9] "WY_ratio_VOLCFNET_ACRE_live_FORTYPNM_STDSZNM_timberland_rawdata_unit_totest.csv"

#### POP2: 2.1 Number of live trees per acre by species, Bighorn National Forest

View Example

We can also use tree domain in estimation output rows:

    ## Number of live trees (at least 1 inch diameter) by species
    ratio2.1 <- modGBratio(
        GBpopdat = GBpopdat.bh,      # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
        estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
        rowvar = "SPCD",             # est - row domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = FALSE,              # out - return output with est and pse
          ),
        title_opts = title_options(
          title.ref = "Bighorn National Forest")
        )

And we can look at our output:

    ## Look at output list
    names(ratio2.1)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio2.1$est)

output

    ##            Species Estimate Percent Sampling Error
    ## 1    quaking aspen     16.1                  83.82
    ## 2      limber pine      7.4                  89.98
    ## 3   lodgepole pine    284.5                  17.82
    ## 4    subalpine fir      114                  28.76
    ## 5 Engelmann spruce     87.1                  26.54
    ## 6      Douglas-fir     42.2                  71.82

#### POP2: 2.2 Number of live trees (plus seedlings) per acre by species, Bighorn National Forest

View Example

Now, we can add seedlings.

Note: seedling data are only available for number of trees (`estvarn = TPA_UNADJ`).

Note: must include seedling data in population data calculations.

    ## Number of live trees by species, including seedlings
    ratio2.2 <- modGBratio(
        GBpopdat = GBpopdat.bh,        # pop - population calculations
        estseed = "add",               # est - add seedling data
        landarea = "FOREST",           # est - forest land filter
        sumunits = TRUE,               # est - sum estimation units to population
        estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
        estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
        rowvar = "SPCD",               # est - row domain
        returntitle = TRUE,            # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = FALSE               # out - return output with est and pse
          ),
        title_opts = title_options(
          title.ref = "Bighorn National Forest")
        )
    ratio2.2$titlelst

output

    ## $title.estpse
    ## [1] "Number of trees (including seedlings) per acre, in trees, and percent sampling error on forest land by species withseeds"
    ##
    ## $title.yvar
    ## [1] "Number, in trees"
    ##
    ## $title.estvar
    ## [1] "Number of trees (including seedlings) per acre"
    ##
    ## $title.yvard
    ## [1] "Acres"
    ##
    ## $title.unitvar
    ## [1] "ONEUNIT"
    ##
    ## $title.ref
    ## [1] "Bighorn National Forest"
    ##
    ## $outfn.estpse
    ## [1] "ratio_TPA_UNADJ_ACRE_withseeds_COMMON_NAME_forestland"
    ##
    ## $outfn.rawdat
    ## [1] "ratio_TPA_UNADJ_ACRE_withseeds_COMMON_NAME_forestland_rawdata"
    ##
    ## $outfn.param
    ## [1] "ratio_TPA_UNADJ_ACRE_withseeds_COMMON_NAME_forestland_parameters"
    ##
    ## $title.rowvar
    ## [1] "Species"
    ##
    ## $title.row
    ## [1] "Number of trees (including seedlings) per acre, in trees, on forest land by species withseeds; Bighorn National Forest"
    ##
    ## $title.unitsn
    ## [1] "trees"
    ##
    ## $title.unitsd
    ## [1] "acres"

Output and comparison:

    ## Look at output list
    names(ratio2.2)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio2.2$est)

output

    ##            Species Estimate Percent Sampling Error
    ## 1    quaking aspen    135.1                  75.65
    ## 2      limber pine     60.1                  95.15
    ## 3   lodgepole pine    388.3                  17.07
    ## 4    subalpine fir    635.7                  22.36
    ## 5 Engelmann spruce    193.4                  30.43
    ## 6      Douglas-fir       80                   79.6

    ## Compare estimates with and without seedlings
    head(ratio2.1$est)

output

    ##            Species Estimate Percent Sampling Error
    ## 1    quaking aspen     16.1                  83.82
    ## 2      limber pine      7.4                  89.98
    ## 3   lodgepole pine    284.5                  17.82
    ## 4    subalpine fir      114                  28.76
    ## 5 Engelmann spruce     87.1                  26.54
    ## 6      Douglas-fir     42.2                  71.82

    head(ratio2.2$est)

output

    ##            Species Estimate Percent Sampling Error
    ## 1    quaking aspen    135.1                  75.65
    ## 2      limber pine     60.1                  95.15
    ## 3   lodgepole pine    388.3                  17.07
    ## 4    subalpine fir    635.7                  22.36
    ## 5 Engelmann spruce    193.4                  30.43
    ## 6      Douglas-fir       80                   79.6

#### POP2: 2.3 Number of live seedlings per acre by species, Bighorn National Forest

View Example

We could also consider only seedlings.

Note: seedling data are only available for number of trees (`estvarn = TPA_UNADJ`).

Note: must include seedling data in population data calculations.

    ## Number of live seedlings by species
    ratio2.3 <- modGBratio(
        GBpopdat = GBpopdat.bh,         # pop - population calculations
        estseed = "only",               # est - add seedling data
        landarea = "FOREST",            # est - forest land filter
        sumunits = TRUE,                # est - sum estimation units to population
        estvarn = "TPA_UNADJ",          # est - number of trees per acre, numerator
        rowvar = "SPCD",                # est - row domain
        returntitle = TRUE,             # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,           # est - row domain names
          allin1 = FALSE                # out - return output with est and pse
          )
        )

Output and comparisons:

    ## Look at output list
    names(ratio2.3)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio2.3$est)

output

    ##                  Species Estimate Percent Sampling Error
    ## 1          quaking aspen     15.1                  83.92
    ## 2            limber pine        8                  80.63
    ## 3         lodgepole pine      284                  17.68
    ## 4          subalpine fir    127.7                  29.07
    ## 5       Engelmann spruce     91.4                  25.04
    ## 6 Rocky Mountain juniper       --                     --

    ## Compare estimates with, without, and only seedlings
    head(ratio2.1$est)

output

    ##            Species Estimate Percent Sampling Error
    ## 1    quaking aspen     16.1                  83.82
    ## 2      limber pine      7.4                  89.98
    ## 3   lodgepole pine    284.5                  17.82
    ## 4    subalpine fir      114                  28.76
    ## 5 Engelmann spruce     87.1                  26.54
    ## 6      Douglas-fir     42.2                  71.82

    head(ratio2.2$est)

output

    ##            Species Estimate Percent Sampling Error
    ## 1    quaking aspen    135.1                  75.65
    ## 2      limber pine     60.1                  95.15
    ## 3   lodgepole pine    388.3                  17.07
    ## 4    subalpine fir    635.7                  22.36
    ## 5 Engelmann spruce    193.4                  30.43
    ## 6      Douglas-fir       80                   79.6

    head(ratio2.3$est)

output

    ##                  Species Estimate Percent Sampling Error
    ## 1          quaking aspen     15.1                  83.92
    ## 2            limber pine        8                  80.63
    ## 3         lodgepole pine      284                  17.68
    ## 4          subalpine fir    127.7                  29.07
    ## 5       Engelmann spruce     91.4                  25.04
    ## 6 Rocky Mountain juniper       --                     --

#### POP2: 2.4 Number of live trees by forest type and species, Bighorn National Forest

View Example

We can also use tree domain in estimation output columns:

    ## Number of live trees (at least 1 inch diameter) by forest type and species
    ratio2.4 <- modGBratio(
        GBpopdat = GBpopdat.bh,         # pop - population calculations
        landarea = "FOREST",            # est - forest land filter
        sumunits = TRUE,                # est - sum estimation units to population
        estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
        estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
        rowvar = "FORTYPCD",            # est - row domain
        colvar = "SPCD",                # est - column domain
        returntitle = TRUE,             # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,           # est - row domain names
          col.FIAname = TRUE,           # est - column domain names
          allin1 = TRUE                 # out - return output with est(pse)
          )
        )

And view our output:

    ## Look at output list
    names(ratio2.4)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio2.4$est)

output

    ##                        Forest type  subalpine fir Engelmann spruce
    ## 1                      Douglas-fir    -- (    --)      -- (    --)
    ## 2                 Engelmann spruce  88.6 ( 62.89)   209.8 ( 22.85)
    ## 3 Engelmann spruce / subalpine fir 390.1 ( 36.28)   175.1 ( 47.50)
    ## 4                    Subalpine fir 247.9 ( 61.37)     2.6 ( 73.64)
    ## 5                   Lodgepole pine  67.2 ( 34.42)    70.3 ( 40.77)
    ## 6                            Aspen    -- (    --)      -- (    --)
    ##   lodgepole pine    limber pine    Douglas-fir quaking aspen          Total
    ## 1    -- (    --) 108.1 ( 68.12) 535.4 ( 54.74)   -- (    --) 643.5 ( 56.99)
    ## 2  71.5 ( 81.45)    -- (    --)  54.1 ( 83.86)   -- (    --) 424.0 ( 12.33)
    ## 3    -- (    --)   5.2 ( 82.44)  20.6 ( 60.31)   -- (    --) 590.9 ( 26.07)
    ## 4  55.5 ( 57.28)    -- (    --)    -- (    --)   -- (    --) 306.1 ( 60.55)
    ## 5 390.4 ( 15.86)    -- (    --)   0.6 (100.62) 24.7 ( 82.69) 553.1 ( 10.85)
    ## 6 599.7 (    --)    -- (    --)    -- (    --)   -- (    --) 599.7 (    --)

#### POP2: 2.5 Number of standing dead trees by species and cause of death, Bighorn National Forest

View Example

Next, we look at dead trees:

    ## Net cubic-foot volume of dead trees (at least 5 inches diameter) by species and cause of death
    ratio2.5 <- modGBratio(
        GBpopdat = GBpopdat.bh,        # pop - population calculations
        landarea = "FOREST",           # est - forest land filter
        sumunits = TRUE,               # est - sum estimation units to population
        estvarn = "VOLCFNET",          # est - number of trees per acre, numerator
        estvarn.filter = "STATUSCD == 2 & STANDING_DEAD_CD == 1",    # est - standing dead trees only, numerator
        rowvar = "SPCD",               # est - row domain
        colvar = "AGENTCD",            # est - column domain
        returntitle = TRUE,            # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          col.FIAname = TRUE,          # est - column domain names
          allin1 = TRUE                # out - return output with est(pse)
          )
        )

And we can see our output:

    ## Look at output list
    names(ratio2.5)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio2.5$est)

output

    ##            Species          Other        Insect       Disease          Fire
    ## 1      limber pine   0.1 (100.37)   -- (    --)   -- (    --)  4.5 (100.37)
    ## 2   lodgepole pine  91.4 ( 28.53)  6.2 ( 66.27)  2.5 (100.37)   -- (    --)
    ## 3    subalpine fir  31.8 ( 48.54)  3.8 (100.37) 45.8 ( 61.05)  4.0 ( 99.20)
    ## 4 Engelmann spruce  62.3 ( 44.05) 82.7 ( 97.30)  3.4 ( 67.44) 49.4 ( 99.20)
    ## 5      Douglas-fir  21.0 (100.37)   -- (    --)   -- (    --)   -- (    --)
    ## 6            Total 206.7 ( 28.20) 92.7 ( 90.84) 51.8 ( 59.59) 58.0 ( 91.62)
    ##   Unidentified animal      Weather Vegetation (e.g., suppression)  Unidentified
    ## 1         -- (    --)  -- (    --)                    -- (    --)  0.2 (100.37)
    ## 2        4.7 ( 82.39) 1.1 ( 97.36)                    -- (    --) 19.0 ( 60.58)
    ## 3         -- (    --) 0.8 ( 99.20)                   0.4 (100.37) 11.8 ( 64.97)
    ## 4         -- (    --) 0.9 (100.37)                    -- (    --) 12.4 ( 80.26)
    ## 5         -- (    --)  -- (    --)                    -- (    --)   -- (    --)
    ## 6        4.7 ( 82.39) 2.8 ( 55.87)                   0.4 (100.37) 43.4 ( 39.35)
    ##            Total
    ## 1   4.8 ( 96.32)
    ## 2 124.9 ( 27.57)
    ## 3  98.5 ( 42.92)
    ## 4 211.3 ( 48.90)
    ## 5  21.0 (100.37)
    ## 6 460.5 ( 27.78)

#### POP3.1: 3.1 Number of live trees by species group and diameter class (DIACL2IN), Bighorn National Forest

View Example

We can also use tree domain in estimation output rows and columns:

    ## Number of live trees by species group and diameter class (DIACL2IN)
    ratio2.6 <- modGBratio(
        GBpopdat = GBpopdat.bh,          # pop - population calculations
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
        estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
        rowvar = "SPGRPCD",          # est - row domain
        colvar = "DIACL2IN",         # est - column domain
        returntitle = TRUE,          # out - return title information
        table_opts = list(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = TRUE               # out - return output with est(pse)
          ),
        title_opts = list(
          title.ref = "Bighorn National Forest"
          )
        )

And examine our output:

    ## Look at output list
    names(ratio2.6)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio2.6$est)

output

    ##                 Species group       1.0-2.9       3.0-4.9       5.0-6.9
    ## 1                 Douglas-fir 22.3 (100.37)  8.9 ( 59.75)  3.2 ( 46.01)
    ## 2                    True fir 53.7 ( 32.92) 35.7 ( 40.47)  9.7 ( 31.02)
    ## 3 Engelmann and other spruces 29.3 ( 50.99) 11.2 ( 59.48) 12.4 ( 28.93)
    ## 4              Lodgepole pine 64.7 ( 35.42) 51.5 ( 32.89) 56.0 ( 22.45)
    ## 5     Other western softwoods  4.5 (100.37)   -- (    --)  1.8 ( 82.12)
    ## 6        Cottonwood and aspen  8.9 (100.37)  2.2 (100.37)  3.4 ( 54.08)
    ##         7.0-8.9      9.0-10.9     11.0-12.9    13.0-14.9    15.0-16.9
    ## 1  2.3 ( 59.95)  1.1 ( 56.69)  0.2 (100.37) 0.5 ( 73.82) 0.5 (100.37)
    ## 2  4.3 ( 35.49)  1.3 ( 33.91)  2.0 ( 42.85) 0.2 (101.90) 0.2 ( 99.20)
    ## 3  9.7 ( 33.61)  5.2 ( 35.15)  6.1 ( 27.02) 3.1 ( 37.39) 1.3 ( 64.24)
    ## 4 49.3 ( 19.64) 26.7 ( 18.62) 10.8 ( 20.67) 4.8 ( 29.27) 1.6 ( 40.50)
    ## 5  0.5 ( 73.82)  0.2 (100.37)   -- (    --)  -- (    --)  -- (    --)
    ## 6  0.4 ( 69.79)  0.2 (100.37)   -- (    --)  -- (    --)  -- (    --)
    ##      17.0-18.9    19.0-20.9    21.0-22.9    23.0-24.9          Total
    ## 1  -- (    --) 0.2 (100.37) 0.2 (100.37) 0.2 (100.37)  39.7 ( 71.93)
    ## 2 0.2 (100.37)  -- (    --)  -- (    --)  -- (    --) 107.1 ( 29.07)
    ## 3 1.4 ( 54.08) 1.6 ( 41.09) 0.2 (100.37) 0.4 ( 99.20)  81.8 ( 26.99)
    ## 4 1.3 ( 73.50) 0.5 ( 74.51)  -- (    --) 0.2 (100.37) 267.3 ( 18.33)
    ## 5  -- (    --)  -- (    --)  -- (    --)  -- (    --)   7.0 ( 90.08)
    ## 6  -- (    --)  -- (    --)  -- (    --)  -- (    --)  15.1 ( 83.92)

#### POP3: 3.2 Number of Live Trees per acre by Species Group and Diameter Class, Bighorn National Forest Districts

View Example

Next, we can look at the number of live trees by species group and diameter class (`DIACL`):

    ratio3.2 <- modGBratio(
        GBpopdat = GBpopdat.bhdist,          # pop - population calculations
        landarea = "FOREST",                 # est - forest land filter
        sumunits = TRUE,                     # est - sum estimation units to population
        estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
        estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
        rowvar = "SPGRPCD",                  # est - row domain
        returntitle = TRUE,                  # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,                # est - row domain names
          allin1 = TRUE,                     # out - return output with est(pse)
          ),
        title_opts = title_options(
          title.ref="Bighorn National Forest Districts"
          )
        )

And of course examine our output:

    ## Look at output list
    names(ratio3.2)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio3.2$est)

output

    ##                 Species group Estimate (% Sampling Error)
    ## 1                 Douglas-fir                49.2 (73.91)
    ## 2                    True fir               116.9 (27.81)
    ## 3 Engelmann and other spruces                84.4 (28.70)
    ## 4              Lodgepole pine               277.4 (17.68)
    ## 5     Other western softwoods                 9.2 (87.36)
    ## 6        Cottonwood and aspen                14.4 (81.95)

#### POP1: 1.4 Net cubic-foot volume of live trees (at least 5 inches diameter) divided by net cubic-foot volume of all trees by forest type, Wyoming, 2011-2013

View Example

Next, we look at tree ratios:

    ## Net cubic-foot volume of live trees (at least 5 inches diameter) divided by net cubic-foot volume of all trees
    ##    by forest type, Wyoming, 2011-2013
    ratio1.4 <- modGBratio(
        GBpopdat = GBpopdat,         # pop - population calculations for WY, post-stratification
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvarn = "VOLCFNET",                # est - net cubic-foot volume, numerator
        estvarn.filter = "STATUSCD == 1",    # est - live trees only
        estvard = "VOLCFNET",                # est - net cubic-foot volume, numerator
        rowvar = "FORTYPCD",         # est - row domain
        returntitle = TRUE,          # out - return title information
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = TRUE,               # out - return output with est(pse)
          )
        )

And examine the output:

    ## Look at output list
    names(ratio1.4)

output

    ## [1] "est"      "titlelst" "raw"      "statecd"  "invyr"

    ## Estimate and percent sampling error of estimate
    head(ratio1.4$est)

output

    ##                 Forest type Estimate (% Sampling Error)
    ## 1    Rocky Mountain juniper               383.3 (20.24)
    ## 2          Juniper woodland               677.3 (19.49)
    ## 3 Pinyon / juniper woodland               421.5 (   --)
    ## 4               Douglas-fir             1,691.6 (13.70)
    ## 5            Ponderosa pine             1,347.4 (10.57)
    ## 6          Engelmann spruce             3,137.5 (16.94)

### No strata

If you want to exclude post-stratification (i.e., simple random sample, Horvitz-Thompson), you must generate a new population dataset using the `modGBpop` function by setting `strata = FALSE`.

View Code

    ## Get population data for Wyoming estimates, with no post-stratification
    GBpopdat.strat <- modGBpop(
      popTabs = popTables(
        cond = WYcond,               # FIA plot/condition data
        tree = WYtree,               # FIA tree data
        seed = WYseed),              # FIA seedling data
        pltassgn = WYpltassgn,       # plot assignments
        pltassgnid = "CN",           # uniqueid of plots
        unitarea = WYunitarea,       # area by estimation units
        unitvar = "ESTN_UNIT",       # name of estimation unit
        strata = TRUE,               # if using post-stratification
        stratalut = WYstratalut,     # strata classes and pixels counts
        strata_opts = list(
          getwt=TRUE,                # calculate strata weights
          getwtvar="P1POINTCNT")     # use P1POINTCNT in stratalut to calculate weights
        )

    ## Get population data for Wyoming estimates, with no post-stratification
    GBpopdat.nostrat <- modGBpop(
        popTabs = popTables(
        cond = WYcond,               # FIA plot/condition data
        tree = WYtree,               # FIA tree data
        seed = WYseed),              # FIA seedling data
        pltassgn = WYpltassgn,       # plot assignments
        pltassgnid = "CN",           # uniqueid of plots
        unitarea = WYunitarea,       # area by estimation units
        unitvar = "ESTN_UNIT",       # name of estimation unit
        strata = FALSE               # if using post-stratification
        )

    ## Area of forest land by forest type and stand-size class, Wyoming, 2011-2013, with post-stratification
    area.strat <- modGBarea(
        GBpopdat = GBpopdat.strat,      # pop - population calculations for WY, post-stratification
        landarea = "FOREST",            # est - forest land filter
        sumunits = TRUE,                # est - sum estimation units to population
        rowvar = "FORTYPCD",            # est - row domain
        table_opts = table_options(
          row.FIAname = TRUE,           # est - row domain names
          allin1 = FALSE                # out - return output with est(pse)
          )
        )

    ## Area of forest land by forest type and stand-size class, Wyoming, 2011-2013, no post-stratification
    area.nostrat <- modGBarea(
        GBpopdat = GBpopdat.nostrat,    # pop - population calculations for WY, no post-stratification
        landarea = "FOREST",            # est - forest land filter
        sumunits = TRUE,                # est - sum estimation units to population
        rowvar = "FORTYPCD",            # est - row domain
        table_opts = table_options(
          row.FIAname = TRUE,           # est - row domain names
          allin1 = FALSE                # out - return output with est(pse)
          )
        )

    ## Compare estimates and percent standard errors with and without post-stratification
    head(area.strat$est)

output

    ##                 Forest type Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper 632481.7                  17.28
    ## 2          Juniper woodland 339749.8                  23.85
    ## 3 Pinyon / juniper woodland  14854.7                    100
    ## 4               Douglas-fir   881189                  14.21
    ## 5            Ponderosa pine 889542.8                  12.82
    ## 6          Engelmann spruce 467196.7                  19.99

    head(area.nostrat$est)

output

    ##                 Forest type Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper 637719.7                  17.17
    ## 2          Juniper woodland 340093.9                   23.8
    ## 3 Pinyon / juniper woodland  14854.7                    100
    ## 4               Douglas-fir 863774.3                  14.62
    ## 5            Ponderosa pine 944562.8                  13.77
    ## 6          Engelmann spruce 473242.2                  20.02

    ## Number of live trees by species, Wyoming, 2011-2013, with post-stratification
    tree.strat <- modGBtree(
        GBpopdat = GBpopdat.strat,      # pop - population calculations for WY, post-stratification
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "TPA_UNADJ",               # est - number of trees per acre, numerator
        estvar.filter = "STATUSCD == 1",    # est - live trees only, numerator
        rowvar = "FORTYPCD",         # est - row domain
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = FALSE                # out - return output with est(pse)
          )
        )

    ## Number of live trees by species, Wyoming, 2011-2013, no post-stratification
    tree.nostrat <- modGBtree(
        GBpopdat = GBpopdat.nostrat, # pop - population calculations for WY, no post-stratification
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvar = "TPA_UNADJ",               # est - number of trees per acre, numerator
        estvar.filter = "STATUSCD == 1",    # est - live trees only, numerator
        rowvar = "FORTYPCD",         # est - row domain
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = FALSE                # out - return output with est(pse)
          )
        )

    ## Compare estimates and percent standard errors with and without post-stratification
    head(tree.strat$est)

output

    ##                 Forest type    Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper  94791336.1                  24.57
    ## 2          Juniper woodland    54049570                  28.36
    ## 3 Pinyon / juniper woodland   1072754.3                    100
    ## 4               Douglas-fir 240480740.8                  21.25
    ## 5            Ponderosa pine 260800542.8                  17.27
    ## 6          Engelmann spruce   201830027                  25.03

    head(tree.nostrat$est)

output

    ##                 Forest type    Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper  95715598.7                  24.12
    ## 2          Juniper woodland  54055007.6                  28.29
    ## 3 Pinyon / juniper woodland   1072754.3                    100
    ## 4               Douglas-fir 238146030.4                  21.41
    ## 5            Ponderosa pine 271234746.9                  17.55
    ## 6          Engelmann spruce 203591491.9                  25.02

    ## Number of live trees per acre by species, Wyoming, 2011-2013, with post-stratification
    ratio.strat <- modGBratio(
        GBpopdat = GBpopdat.strat,   # pop - population calculations for WY, post-stratification
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
        estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
        rowvar = "FORTYPCD",         # est - row domain
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = FALSE                # out - return output with est(pse)
          )
        )

    ## Number of live trees per acre by species, Wyoming, 2011-2013, no post-stratification
    ratio.nostrat <- modGBratio(
        GBpopdat = GBpopdat.nostrat, # pop - population calculations for WY, no post-stratification
        landarea = "FOREST",         # est - forest land filter
        sumunits = TRUE,             # est - sum estimation units to population
        estvarn = "TPA_UNADJ",               # est - number of trees per acre, numerator
        estvarn.filter = "STATUSCD == 1",    # est - live trees only, numerator
        rowvar = "FORTYPCD",         # est - row domain
        table_opts = table_options(
          row.FIAname = TRUE,          # est - row domain names
          allin1 = FALSE                # out - return output with est(pse)
          )
        )

    ## Compare estimates and percent standard errors with and without post-stratification
    head(ratio.strat$est)

output

    ##                 Forest type Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper    149.9                  17.49
    ## 2          Juniper woodland    159.1                   14.1
    ## 3 Pinyon / juniper woodland     72.2                      0
    ## 4               Douglas-fir    272.9                  15.15
    ## 5            Ponderosa pine    293.2                  11.01
    ## 6          Engelmann spruce      432                  15.51

    head(ratio.nostrat$est)

output

    ##                 Forest type Estimate Percent Sampling Error
    ## 1    Rocky Mountain juniper    150.1                   17.1
    ## 2          Juniper woodland    158.9                  14.06
    ## 3 Pinyon / juniper woodland     72.2                      0
    ## 4               Douglas-fir    275.7                  15.09
    ## 5            Ponderosa pine    287.2                  10.79
    ## 6          Engelmann spruce    430.2                  15.34

### By estimation unit (`sumunits = FALSE`)

If you just wanted estimates by estimation unit and did not want to sum them, set `sumunits = FALSE`. If `sumunits = FALSE`, the estimates and percent standard errors returned are by estimation unit, with an attribute, named ‘unit’ appended to data frame, with the unit value. The raw data returned will look the same as if `sumunits = TRUE`.

View Code

    ## By estimation unit
    ## Area of forest land by forest type and stand-size class and Estimation Unit,
    ##    Wyoming, 2011-2013
    ##################################################################################
    area.unit <- modGBarea(
        GBpopdat = GBpopdat,         # pop - population calculations for WY, post-stratification
        landarea = "FOREST",         # est - forest land filter
        sumunits = FALSE,            # est - sum estimation units to population
        rowvar = "FORTYPCD",         # est - row domain
        colvar = "STDSZCD",          # est - column domain
        returntitle = TRUE,          # out - return title information
        table_opts = list(
          allin1 = TRUE)             # out - return output with est(pse)
        )

    ## Estimate and percent sampling error of estimate (first 6 rows)
    head(area.unit$est)

output

    ##   unit Forest type                 1                 2                 3
    ## 1    1         182 19,698.3 (102.67)       -- (    --) 19,698.3 (102.67)
    ## 2    1         201 28,215.6 ( 87.92)       -- (    --)       -- (    --)
    ## 3    1         221 89,329.6 ( 45.13)       -- (    --)       -- (    --)
    ## 4    1         266 56,431.1 ( 60.19) 56,431.1 ( 60.19)       -- (    --)
    ## 5    1         268       -- (    --) 28,215.6 ( 87.92)       -- (    --)
    ## 6    1         281       -- (    --) 84,646.7 ( 47.48) 21,656.7 ( 87.92)
    ##             5              Total
    ## 1 -- (    --)  39,396.6 ( 72.29)
    ## 2 -- (    --)  28,215.6 ( 87.92)
    ## 3 -- (    --)  89,329.6 ( 45.13)
    ## 4 -- (    --) 112,862.2 ( 39.62)
    ## 5 -- (    --)  28,215.6 ( 87.92)
    ## 6 -- (    --) 106,303.3 ( 39.92)

    ## Unique estimation units
    unique(area.unit$est$unit)

output

    ##  [1]  1  3  5  7  9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

## References

Bechtold, William A.; Patterson, Paul L., Editors. 2005. The enhanced Forest Inventory and Analysis program national sampling design and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 85 p.

Patterson, Paul L. 2012. Photo-based estimators for the Nevada photo-based inventory. Res. Pap. RMRS-RP-92. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 14 p.

Westfall, James A.; Patterson, Paul L.; Coulston, John W. 2011. Post-stratified estimation: with-in strata and total sample size recommendations. Canadian Journal of Forest Research. 41: 1130-1139.