BetaStability with vegan datasets
This vignette demonstrates the BetaStability package using
linearPred with multiple datasets (BCI, dune, and mite)
from the vegan package.
Installation
First, install the package from GitHub:
# Install from GitHub
# install.packages("devtools")
# devtools::install_github("gaoyu19920914/betaStability")
# OR install from BioConductor (in the future when it's available)
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("betaStability")Loading Required Packages
Load the BetaStability package and the vegan package for test data:
Demonstrating General Applicability with linearPred
df_prepare <- function(df) {
# Remove columns where all elements are the same
df <- df[, vapply(df, function(x) length(unique(x)) > 1, logical(1))]
# Convert columns to integer if they contain different types of strings
for (col in names(df)) {
if (is.character(df[[col]]) && length(unique(df[[col]])) > 1) {
df[[col]] <- as.integer(as.factor(df[[col]]))
}
}
return(df)
}BCI Dataset with linearPred
Calculate stability for BCI dataset using
linearPred:
# Calculate stability with linearPred
stability_BCI_linear <- betaStability(
comtable = BCI,
envmeta = BCI.env[, c("Precipitation", "Elevation", "EnvHet")],
method = "linearPred"
)
# Inspect the result
head(stability_BCI_linear)
#> stability_Linear
#> 1 0.053679327
#> 2 0.118797973
#> 3 0.052407524
#> 4 0.087186422
#> 5 -0.034849722
#> 6 0.001645529
length(stability_BCI_linear)
#> [1] 1Dune Dataset with linearPred
Load the dune (community data) and dune.env
(environmental metadata) datasets from the vegan package, then calculate
stability:
data(dune)
data(dune.env)
# Inspect the data
head(dune)
#> Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord Chenalbu
#> 1 1 0 0 0 0 0 0 0
#> 2 3 0 0 2 0 3 4 0
#> 3 0 4 0 7 0 2 0 0
#> 4 0 8 0 2 0 2 3 0
#> 5 2 0 0 0 4 2 2 0
#> 6 2 0 0 0 3 0 0 0
#> Cirsarve Comapalu Eleopalu Elymrepe Empenigr Hyporadi Juncarti Juncbufo
#> 1 0 0 0 4 0 0 0 0
#> 2 0 0 0 4 0 0 0 0
#> 3 0 0 0 4 0 0 0 0
#> 4 2 0 0 4 0 0 0 0
#> 5 0 0 0 4 0 0 0 0
#> 6 0 0 0 0 0 0 0 0
#> Lolipere Planlanc Poaprat Poatriv Ranuflam Rumeacet Sagiproc Salirepe
#> 1 7 0 4 2 0 0 0 0
#> 2 5 0 4 7 0 0 0 0
#> 3 6 0 5 6 0 0 0 0
#> 4 5 0 4 5 0 0 5 0
#> 5 2 5 2 6 0 5 0 0
#> 6 6 5 3 4 0 6 0 0
#> Scorautu Trifprat Trifrepe Vicilath Bracruta Callcusp
#> 1 0 0 0 0 0 0
#> 2 5 0 5 0 0 0
#> 3 2 0 2 0 2 0
#> 4 2 0 1 0 2 0
#> 5 3 2 2 0 2 0
#> 6 3 5 5 0 6 0
head(dune.env)
#> A1 Moisture Management Use Manure
#> 1 2.8 1 SF Haypastu 4
#> 2 3.5 1 BF Haypastu 2
#> 3 4.3 2 SF Haypastu 4
#> 4 4.2 2 SF Haypastu 4
#> 5 6.3 1 HF Hayfield 2
#> 6 4.3 1 HF Haypastu 2
# Dimensions of the datasets
cat("Dimensions of dune:", dim(dune), "\n")
#> Dimensions of dune: 20 30
cat("Dimensions of dune.env:", dim(dune.env), "\n")
#> Dimensions of dune.env: 20 5
# Process environmental data using df_prepare
dune.env_processed <- df_prepare(dune.env)
# Calculate stability with linearPred
stability_dune_linear <- betaStability(
comtable = dune,
envmeta = dune.env_processed,
method = "linearPred"
)
#> Warning in dist(BBmisc::normalize(envmeta, method = "range", margin = 2), : NAs
#> introduced by coercion
# Inspect the result
head(stability_dune_linear)
#> stability_Linear
#> 1 -0.10941041
#> 2 0.06295745
#> 3 0.20765436
#> 4 0.15511150
#> 5 0.06873168
#> 6 0.06454557
length(stability_dune_linear)
#> [1] 1Mite Dataset with linearPred
Load the mite (community data) and mite.env
(environmental metadata) datasets from the vegan package, then calculate
stability:
data(mite)
data(mite.env)
# Inspect the data
head(mite)
#> Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA TVEL ONOV
#> 1 17 5 5 3 2 1 4 2 2 1 4 1 17 4
#> 2 2 7 16 0 6 0 4 2 0 0 1 3 21 27
#> 3 4 3 1 1 2 0 3 0 0 0 6 3 20 17
#> 4 23 7 10 2 2 0 4 0 1 2 10 0 18 47
#> 5 5 8 13 9 0 13 0 0 0 3 14 3 32 43
#> 6 19 7 5 9 3 2 3 0 0 20 16 2 13 38
#> SUCT LCIL Oribatl1 Ceratoz1 PWIL Galumna1 Stgncrs2 HRUF Trhypch1 PPEL NCOR
#> 1 9 50 3 1 1 8 0 0 0 0 0
#> 2 12 138 6 0 1 3 9 1 1 1 2
#> 3 10 89 3 0 2 1 8 0 3 0 2
#> 4 17 108 10 1 0 1 2 1 2 1 3
#> 5 27 5 1 0 5 2 1 0 1 0 0
#> 6 39 3 5 0 1 1 8 0 4 0 1
#> SLAT FSET Lepidzts Eupelops Miniglmn LRUG PLAG2 Ceratoz3 Oppiminu Trimalc2
#> 1 0 0 0 0 0 0 0 0 0 0
#> 2 2 2 1 0 0 0 0 0 0 0
#> 3 0 8 0 0 0 0 0 0 0 0
#> 4 2 12 0 0 0 0 0 0 0 0
#> 5 0 12 2 0 0 0 0 0 0 0
#> 6 0 10 0 0 0 0 0 0 0 0
head(mite.env)
#> SubsDens WatrCont Substrate Shrub Topo
#> 1 39.18 350.15 Sphagn1 Few Hummock
#> 2 54.99 434.81 Litter Few Hummock
#> 3 46.07 371.72 Interface Few Hummock
#> 4 48.19 360.50 Sphagn1 Few Hummock
#> 5 23.55 204.13 Sphagn1 Few Hummock
#> 6 57.32 311.55 Sphagn1 Few Hummock
# Dimensions of the datasets
cat("Dimensions of mite:", dim(mite), "\n")
#> Dimensions of mite: 70 35
cat("Dimensions of mite.env:", dim(mite.env), "\n")
#> Dimensions of mite.env: 70 5
# Process environmental data using df_prepare
mite.env_processed <- df_prepare(mite.env)
# Calculate stability with linearPred
stability_mite_linear <- betaStability(
comtable = mite,
envmeta = mite.env_processed,
method = "linearPred"
)
#> Warning in dist(BBmisc::normalize(envmeta, method = "range", margin = 2), : NAs
#> introduced by coercion
# Inspect the result
head(stability_mite_linear)
#> stability_Linear
#> 1 0.0246162099
#> 2 -0.0047508295
#> 3 -0.0001991863
#> 4 0.0161315321
#> 5 0.0528486489
#> 6 0.0533943609
length(stability_mite_linear)
#> [1] 1
Summary
General applicability across different datasets:
Using the simple and fast linearPred method, we
successfully calculated stability for three different datasets (BCI,
dune, and mite) representing different ecological systems. This
demonstrates the versatility of the BetaStability package across various
types of ecological data.
print(sessionInfo())
#> R version 4.5.3 (2026-03-11 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26200)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: Asia/Shanghai
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_4.0.2 vegan_2.7-3 permute_0.9-10
#> [4] betaStability_0.0.4
#>
#> loaded via a namespace (and not attached):
#> [1] sass_0.4.10 generics_0.1.4 shape_1.4.6.1
#> [4] stringi_1.8.7 lattice_0.22-9 usedist_0.4.0
#> [7] digest_0.6.39 magrittr_2.0.4 rioja_1.0-7
#> [10] evaluate_1.0.5 grid_4.5.3 RColorBrewer_1.1-3
#> [13] BBmisc_1.13.1 iterators_1.0.14 fastmap_1.2.0
#> [16] xgboost_3.2.1.1 glmnet_4.1-10 Matrix_1.7-5
#> [19] foreach_1.5.2 doParallel_1.0.17 plyr_1.8.9
#> [22] jsonlite_2.0.0 backports_1.5.0 survival_3.8-6
#> [25] mgcv_1.9-4 scales_1.4.0 pbapply_1.7-4
#> [28] codetools_0.2-20 jquerylib_0.1.4 cli_3.6.5
#> [31] rlang_1.1.7 splines_4.5.3 withr_3.0.2
#> [34] cachem_1.1.0 yaml_2.3.12 otel_0.2.0
#> [37] tools_4.5.3 parallel_4.5.3 reshape2_1.4.5
#> [40] checkmate_2.3.4 dplyr_1.2.0 gdm_1.6.0-7
#> [43] vctrs_0.7.2 R6_2.6.1 lifecycle_1.0.5
#> [46] randomForest_4.7-1.2 stringr_1.6.0 MASS_7.3-65
#> [49] cluster_2.1.8.2 pkgconfig_2.0.3 pillar_1.11.1
#> [52] bslib_0.10.0 gtable_0.3.6 data.table_1.18.2.1
#> [55] glue_1.8.0 Rcpp_1.1.1 xfun_0.57
#> [58] tibble_3.3.1 tidyselect_1.2.1 rstudioapi_0.18.0
#> [61] knitr_1.51 farver_2.1.2 nlme_3.1-169
#> [64] htmltools_0.5.9 labeling_0.4.3 rmarkdown_2.31
#> [67] compiler_4.5.3 S7_0.2.1