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R package designed to afford exploratory and confirmatory estimation of 3 types of moderated networks:
systemfit, but OLS is also available (unconstrained SUR
estimates are equivalent to OLS estimates).lmerAdditionally, model selection techniques based on penalized regression and iterative resampling are implemented alongside a variety of plotting and data-generating functions.
You can install the development version of modnets from GitHub with:
# install.packages('devtools')
devtools::install_github('tswanson222/modnets')library(modnets)
data <- na.omit(psychTools::msq[, c('hostile', 'lonely', 'nervous', 'sleepy', 'depressed')])
fit <- fitNetwork(data, moderators = 'depressed')
plot(fit, threshold = TRUE)
fitNetwork. There are a variety of arguments and options
that can be used for, e.g., penalized estimation, model specification,
etc.mlGVAR and lmerVAR, depending on which
approach you wish to use.varSelect to employ
either best-subset selection (via leaps), the LASSO, ridge
regression, or elastic net (via glmnet), or the
hierarchical LASSO (via glinternet). These methods support
various information criteria as well as cross-validation for model
selection, and are embedded within the varSelect
function.varSelect operate on a sequential, nodewise basis.resample function to use
repeated subsampling or bootstrapping with the varSelect
algorithm built in.bootNet function.bootNet while setting caseDrop = TRUE.mnetPowerSim to
simulate data based on expected network structure(s).See the package description for an overview of these and other core functions.
library(modnets)
?modnetslibrary(modnets)
# Package is now loaded!
### ================================================ ###
### ======= SIMULATE MODERATED NETWORK DATA ======== ###
### ================================================ ###
# Can simulate data with no moderators, or with one exogenous moderator
set.seed(123)
x <- simNet(N = 500, p = 5, m = TRUE, m1 = .5, m2 = .2)
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#> Time difference of 21.95177 secs
str(x)
#> List of 6
#> $ data :'data.frame': 500 obs. of 6 variables:
#> ..$ V1: num [1:500] 0.25 -1.22 -0.396 -1.203 -0.383 ...
#> ..$ V2: num [1:500] 1.294 0.863 0.579 3.47 0.766 ...
#> ..$ V3: num [1:500] -0.355 0.12 0.102 -1.765 -0.218 ...
#> ..$ V4: num [1:500] -0.0265 1.3573 -1.543 -1.2674 0.5639 ...
#> ..$ V5: num [1:500] 0.742 -1.374 1.199 2.171 0.538 ...
#> ..$ M : num [1:500] -0.0154 -2.0282 -0.3164 -1.5306 -1.8338 ...
#> $ b1 : num [1:5, 1:5] 0 0 -0.445 0 0 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : NULL
#> $ b2 : num [1:5, 1:5] 0 0 0 0 0.193 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : NULL
#> $ intercepts: num [1:5] 0 0 0 0 0
#> $ m : num -0.56
#> $ m1 : num [1:5] 0.233 -0.34 -0.137 0 -0.162
#> - attr(*, "m2")= num 2
#> - attr(*, "modType")= chr "none"
#> - attr(*, "class")= chr [1:2] "list" "ggmSim"
#> - attr(*, "time")= 'difftime' num 21.9515688419342
#> ..- attr(*, "units")= chr "secs"
### Contents:
# x$data -------- 500x6 dataset, where 'M' is the moderator
# x$b1 ---------- true regression coefficients, where columns --> rows
# x$b2 ---------- true interaction coefficients, where (M * columns) --> rows
# x$intercepts -- true intercepts; defaults to 0
# x$m ----------- true mean of 'M'
# x$m1 ---------- coefficents for main effects of M on outcomes; default to 0
head(x$data)
#> V1 V2 V3 V4 V5 M
#> 1 0.2503133 1.2941247 -0.3551904 -0.0265180 0.7419560 -0.01537839
#> 2 -1.2199405 0.8633412 0.1199374 1.3572920 -1.3739953 -2.02821907
#> 3 -0.3964995 0.5792081 0.1020903 -1.5429760 1.1990735 -0.31635159
#> 4 -1.2029323 3.4699356 -1.7648713 -1.2674076 2.1714251 -1.53060311
#> 5 -0.3833459 0.7655554 -0.2175814 0.5639044 0.5379368 -1.83380340
#> 6 -1.8996645 1.4409709 -0.1385961 -0.0816034 2.0968175 -0.19894797
print(x$b1)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.000000 0.0000000 -0.4449240 0.0000000 0.0000000
#> [2,] 0.000000 0.0000000 0.2829328 0.0000000 0.3307213
#> [3,] -0.444924 0.2829328 0.0000000 0.2991120 0.0000000
#> [4,] 0.000000 0.0000000 0.2991120 0.0000000 -0.3089766
#> [5,] 0.000000 0.3307213 0.0000000 -0.3089766 0.0000000
print(x$b2)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.0000000 0 0.0000000 0 0.1931925
#> [2,] 0.0000000 0 0.0000000 0 0.0000000
#> [3,] 0.0000000 0 0.0000000 0 0.2715655
#> [4,] 0.0000000 0 0.0000000 0 0.0000000
#> [5,] 0.1931925 0 0.2715655 0 0.0000000
print(x$intercepts)
#> [1] 0 0 0 0 0
print(x$m)
#> [1] -0.5604756
print(x$m1)
#> [1] 0.2326600 -0.3396775 -0.1365698 0.0000000 -0.1619594
dat0 <- x$data[, -6]
dat1 <- x$data
# First, lets save an object that excludes the moderator (dat0)
# and save a second with the moderator (dat1)
### ================================================ ###
### =============== FITTING MODELS ================= ###
### ================================================ ###
# First, lets fit an unmoderated network, leaving out 'M' entirely
fit0 <- fitNetwork(data = dat0)
# Next, lets fit a model that only includes 'M' as a covariate
fit1 <- fitNetwork(data = dat1, covariates = 6)
# Now, lets fit the saturated model where 'M' moderates all edges in the network
fit2 <- fitNetwork(data = dat1, moderators = 6)
### ================= PLOTTING ===================== ###
plot(fit0)
plot(fit1)
plot(fit2)
# We can plot each of these models to see the resultant undirected network
plot(fit0, threshold = .05)
plot(fit1, threshold = .05)
plot(fit2, threshold = .05)
# Plot only significant edges (p < threshold) of the network.
plot(fit0, threshold = TRUE, predict = TRUE)
plot(fit1, threshold = TRUE, predict = 'R2')
plot(fit2, threshold = TRUE, predict = 'adjR2')
# Using 'threshold = TRUE' is the same as 'threshold = .05'
# 'predict = TRUE' plots R2 values for each regression
# This can also be specified as a string, as shown
plot(fit2, threshold = TRUE, predict = fit0)
# This can also be used to visually compare networks
# Here, the light blue ring around each node shows
# the R2 for 'fit0', while the slightly darker piece
# shows the increase in R2 that we see with 'fit2'
predictNet(fit2)
#> Variable R2 adjR2 MSE RMSE
#> 1 V1 0.279 0.265 1.022 1.011
#> 2 V2 0.314 0.301 1.071 1.035
#> 3 V3 0.428 0.418 1.059 1.029
#> 4 V4 0.278 0.265 0.955 0.977
#> 5 V5 0.295 0.282 1.103 1.050
predictNet(fit2, fit0)
#> Variable R2 adjR2 MSE RMSE
#> 1 V1 0.074 0.066 -0.092 -0.044
#> 2 V2 0.080 0.073 -0.112 -0.053
#> 3 V3 0.043 0.038 -0.069 -0.033
#> 4 V4 0.010 0.003 -0.003 -0.002
#> 5 V5 0.045 0.039 -0.059 -0.028
# We can extract these values using this function
# And can take the differences by supplying two networks
# Values for the second model are subtracted from those for the first
plot(fit2, mnet = TRUE)
plot(fit2, threshold = TRUE, mnet = TRUE)
# 'mnet = TRUE' plots the exogenous moderator
### ============= VARIABLE SELECTION =============== ###
# These methods demonstrate the two-stage process for variable selection
# In the first stage, we use the data to select the active set of predictors
# In the second stage, we use those predictors to re-fit the models using OLS
### UNMODERATED NETWORKS
vars0 <- varSelect(data = dat0, criterion = 'BIC', method = 'glmnet')
#> =================================================================================================================================
vfit0 <- fitNetwork(data = dat0, type = vars0)
vfit1 <- fitNetwork(data = dat0, type = 'varSelect', criterion = 'BIC')
predictNet(vfit0, vfit1)
#> Variable R2 adjR2 MSE RMSE
#> 1 V1 0 0 0 0
#> 2 V2 0 0 0 0
#> 3 V3 0 0 0 0
#> 4 V4 0 0 0 0
#> 5 V5 0 0 0 0
# In the first method, we use glmnet to perform variable selection for
# each of the nodewise models. Then, we can subsequently include this in the
# 'fitNetwork' function. In the second approach, we can simply include everything
# in one command. We see that these produce the exact same models
vfit2 <- fitNetwork(data = dat0, type = 'varSelect', criterion = 'BIC', method = 'subset')
# We can also use best-subsets selection instead of the LASSO
predictNet(vfit2, vfit1)
#> Variable R2 adjR2 MSE RMSE
#> 1 V1 -0.001 0.001 -0.001 0.000
#> 2 V2 -0.001 0.001 -0.002 -0.001
#> 3 V3 0.000 0.000 0.000 0.000
#> 4 V4 0.000 0.000 0.000 0.000
#> 5 V5 -0.017 -0.013 0.020 0.009
# In this case, we see that best-subsets produced lower R2 for three nodes
vfit3 <- fitNetwork(data = dat0, type = 'varSelect', criterion = 'CV', seed = 1)
vfit3.1 <- fitNetwork(data = dat0, type = 'varSelect', criterion = 'CV', seed = 1)
vfit3.2 <- fitNetwork(data = dat0, type = 'varSelect', criterion = 'CV', seed = 99)
# We can also use cross-validation with glmnet (but not best-subsets)
predictNet(vfit3, vfit3.1)
#> Variable R2 adjR2 MSE RMSE
#> 1 V1 0 0 0 0
#> 2 V2 0 0 0 0
#> 3 V3 0 0 0 0
#> 4 V4 0 0 0 0
#> 5 V5 0 0 0 0
predictNet(vfit3, vfit3.2)
#> Variable R2 adjR2 MSE RMSE
#> 1 V1 0 0 0 0
#> 2 V2 0 0 0 0
#> 3 V3 0 0 0 0
#> 4 V4 0 0 0 0
#> 5 V5 0 0 0 0
# We see that setting a seed leads to reproducible results
### MODERATED NETWORKS
vars1 <- varSelect(data = dat1, m = 6, criterion = 'BIC', method = 'glinternet')
#> Fitting model 1/5... Complete! (0.13 secs)
#> Fitting model 2/5... Complete! (0.11 secs)
#> Fitting model 3/5... Complete! (0.08 secs)
#> Fitting model 4/5... Complete! (0.07 secs)
#> Fitting model 5/5... Complete! (0.07 secs)
#> ####### Total time: 0.46 secs
mfit1 <- fitNetwork(data = dat1, moderators = 6, type = vars1)
mfit2 <- fitNetwork(data = dat1, moderators = 6, type = 'varSelect', criterion = 'BIC')
predictNet(mfit1, mfit2)
#> Variable R2 adjR2 MSE RMSE
#> 1 V1 0 0 0 0
#> 2 V2 0 0 0 0
#> 3 V3 0 0 0 0
#> 4 V4 0 0 0 0
#> 5 V5 0 0 0 0
# Again, we see that both methods produce the same model
# Creating the 'vars1' object separately can be useful when we wish
# to analyze the results from the variable selection process; plot outputs, obtain coefficients, etc.
# Also, all moderated networks use 'glinternet' as the selection method, and so it does not need to be specified
mfit2 <- fitNetwork(data = dat1, moderators = 6, type = 'varSelect', criterion = 'CV', seed = 1)
# We can use cross-validation with the glinternet algorithm as well
### ============== MODEL COMPARISON ================ ###
# Create a list of models we want to compare
fits <- list(fit0 = fit0, fit1 = fit1, fit2 = fit2,
vfit1 = vfit1, vfit2 = vfit2, vfit3 = vfit3,
mfit1 = mfit1, mfit2 = mfit2)
modTable(fits)
#> $LRT
#> net0 net1 Chisq Df pval decision
#> 1 fit0 fit1 48.7152 5 0.0000 fit1
#> 2 fit0 fit2 147.1725 25 0.0000 fit2
#> 3 vfit1 fit0 0.0178 4 1.0000 vfit1
#> 4 vfit2 fit0 22.7036 8 0.0038 fit0
#> 5 vfit3 fit0 0.0178 4 1.0000 vfit3
#> 6 fit0 mfit1 121.7265 4 0.0000 mfit1
#> 7 fit0 mfit2 140.1842 12 0.0000 mfit2
#> 8 fit1 fit2 98.4573 20 0.0000 fit2
#> 9 vfit1 fit1 48.7330 9 0.0000 fit1
#> 10 vfit2 fit1 71.4188 13 0.0000 fit1
#> 11 vfit3 fit1 48.7330 9 0.0000 fit1
#> 12 mfit1 fit1 73.0112 1 0.0000 mfit1
#> 13 fit1 mfit2 91.4690 7 0.0000 mfit2
#> 14 vfit1 fit2 147.1903 29 0.0000 fit2
#> 15 vfit2 fit2 169.8762 33 0.0000 fit2
#> 16 vfit3 fit2 147.1903 29 0.0000 fit2
#> 17 mfit1 fit2 25.4461 21 0.2284 mfit1
#> 18 mfit2 fit2 6.9883 13 0.9028 mfit2
#> 19 vfit2 vfit1 22.6858 4 0.0001 vfit1
#> 20 vfit1 vfit3 0.0000 0 1.0000 -
#> 21 vfit1 mfit1 121.7443 8 0.0000 mfit1
#> 22 vfit1 mfit2 140.2020 16 0.0000 mfit2
#> 23 vfit2 vfit3 22.6858 4 0.0001 vfit3
#> 24 vfit2 mfit1 144.4301 12 0.0000 mfit1
#> 25 vfit2 mfit2 162.8878 20 0.0000 mfit2
#> 26 vfit3 mfit1 121.7443 8 0.0000 mfit1
#> 27 vfit3 mfit2 140.2020 16 0.0000 mfit2
#> 28 mfit1 mfit2 18.4577 8 0.0180 mfit2
#>
#> $omnibus
#> LL df AIC BIC LRT
#> mfit2 -3400.600 38 6877.199 7098.513 7
#> mfit1 -3409.829 30 6879.657 7054.379 6
#> fit2 -3397.106 51 6896.211 7193.237 5
#> fit1 -3446.334 31 6954.668 7135.214 4
#> vfit1 -3470.701 22 6985.401 7113.530 2
#> vfit3 -3470.701 22 6985.401 7113.530 2
#> fit0 -3470.692 26 6993.384 7144.809 1
#> vfit2 -3482.044 18 7000.087 7104.920 0
#>
#> attr(,"alpha")
#> [1] 0.05
# Performs likelihood ratio tests comparing each model with every other
modTable(fits)$omnibus
#> LL df AIC BIC LRT
#> mfit2 -3400.600 38 6877.199 7098.513 7
#> mfit1 -3409.829 30 6879.657 7054.379 6
#> fit2 -3397.106 51 6896.211 7193.237 5
#> fit1 -3446.334 31 6954.668 7135.214 4
#> vfit1 -3470.701 22 6985.401 7113.530 2
#> vfit3 -3470.701 22 6985.401 7113.530 2
#> fit0 -3470.692 26 6993.384 7144.809 1
#> vfit2 -3482.044 18 7000.087 7104.920 0
# This shows us the final results. The 'LRT' column indicates
# the total number of times each model was selected across all tests
# We can see that 'fit2' (the saturated MNM) was selected across all tests
# The second-most selected was 'mfit2', which used glinternet with CV selection
modTable(fits, nodes = TRUE)
#> $nodes
#> $nodes$V1
#> LL df AIC BIC
#> fit0 -733.9535 495 1479.907 1505.195
#> fit1 -720.1321 494 1454.264 1483.767
#> fit2 -709.7404 490 1441.481 1487.841
#> vfit1 -733.9565 496 1477.913 1498.986
#> vfit2 -734.2404 497 1476.481 1493.339
#> vfit3 -733.9565 496 1477.913 1498.986
#> mfit1 -710.8920 494 1435.784 1465.286
#> mfit2 -710.8920 494 1435.784 1465.286
#>
#> $nodes$V2
#> LL df AIC BIC
#> fit0 -748.9500 495 1509.900 1535.188
#> fit1 -724.3260 494 1462.652 1492.154
#> fit2 -721.4508 490 1464.902 1511.262
#> vfit1 -748.9514 496 1507.903 1528.976
#> vfit2 -749.2353 497 1506.471 1523.329
#> vfit3 -748.9514 496 1507.903 1528.976
#> mfit1 -724.7573 496 1459.515 1480.588
#> mfit2 -721.8251 493 1459.650 1493.367
#>
#> $nodes$V3
#> LL df AIC BIC
#> fit0 -737.0920 495 1486.184 1511.472
#> fit1 -736.8972 494 1487.794 1517.297
#> fit2 -718.8669 490 1459.734 1506.094
#> vfit1 -737.0920 495 1486.184 1511.472
#> vfit2 -737.0920 495 1486.184 1511.472
#> vfit3 -737.0920 495 1486.184 1511.472
#> mfit1 -720.3461 492 1458.692 1496.624
#> mfit2 -718.8669 490 1459.734 1506.094
#>
#> $nodes$V4
#> LL df AIC BIC
#> fit0 -696.3325 495 1404.665 1429.953
#> fit1 -696.0762 494 1406.152 1435.655
#> fit2 -692.7979 490 1407.596 1453.957
#> vfit1 -696.3371 497 1400.674 1417.533
#> vfit2 -696.3371 497 1400.674 1417.533
#> vfit3 -696.3371 497 1400.674 1417.533
#> mfit1 -696.3371 497 1400.674 1417.533
#> mfit2 -693.5546 495 1399.109 1424.397
#>
#> $nodes$V5
#> LL df AIC BIC
#> fit0 -744.4335 495 1500.867 1526.155
#> fit1 -744.4163 494 1502.833 1532.335
#> fit2 -728.9414 490 1479.883 1526.244
#> vfit1 -744.4335 495 1500.867 1526.155
#> vfit2 -749.8211 497 1507.642 1524.501
#> vfit3 -744.4335 495 1500.867 1526.155
#> mfit1 -729.0802 492 1476.160 1514.092
#> mfit2 -728.9428 491 1477.886 1520.032
#>
#>
#> $LRT
#> $LRT$LL_diff2
#> net0 net1 | V1 V2 V3 V4 V5
#> 1 fit0 fit1 | 27.642732993 49.248007033 0.3895996 0.512704268 0.034506600
#> 2 fit0 fit2 | 48.426212747 54.998357118 36.4501942 7.069194569 30.984132425
#> 3 vfit1 fit0 | 0.005980198 0.002913092 0.0000000 0.009184894 0.000000000
#> 4 vfit2 fit0 | 0.573754166 0.570687060 0.0000000 0.009184894 10.775144944
#> 5 vfit3 fit0 | 0.005980198 0.002913092 0.0000000 0.009184894 0.000000000
#> 6 fit0 mfit1 | 46.122928240 48.385466247 33.4917773 0.009184894 30.706638845
#> 7 fit0 mfit2 | 46.122928240 54.249832678 36.4501942 5.555794961 30.981365617
#> 8 fit1 fit2 | 20.783479754 5.750350084 36.0605946 6.556490301 30.949625826
#> 9 vfit1 fit1 | 27.648713191 49.250920125 0.3895996 0.521889162 0.034506600
#> 10 vfit2 fit1 | 28.216487159 49.818694093 0.3895996 0.521889162 10.809651544
#> 11 vfit3 fit1 | 27.648713191 49.250920125 0.3895996 0.521889162 0.034506600
#> 12 mfit1 fit1 | 18.480195247 0.862540787 33.1021777 0.521889162 30.672132246
#> 13 fit1 mfit2 | 18.480195247 5.001825644 36.0605946 5.043090693 30.946859017
#> 14 vfit1 fit2 | 48.432192945 55.001270210 36.4501942 7.078379463 30.984132425
#> 15 vfit2 fit2 | 48.999966913 55.569044177 36.4501942 7.078379463 41.759277369
#> 16 vfit3 fit2 | 48.432192945 55.001270210 36.4501942 7.078379463 30.984132425
#> 17 mfit1 fit2 | 2.303284507 6.612890871 2.9584169 7.078379463 0.277493580
#> 18 mfit2 fit2 | 2.303284507 0.748524440 0.0000000 1.513399608 0.002766809
#> 19 vfit2 vfit1 | 0.567773968 0.567773968 0.0000000 0.000000000 10.775144944
#> 20 vfit1 vfit3 | 0.000000000 0.000000000 0.0000000 0.000000000 0.000000000
#> 21 vfit1 mfit1 | 46.128908438 48.388379339 33.4917773 0.000000000 30.706638845
#> 22 vfit1 mfit2 | 46.128908438 54.252745770 36.4501942 5.564979855 30.981365617
#> 23 vfit2 vfit3 | 0.567773968 0.567773968 0.0000000 0.000000000 10.775144944
#> 24 vfit2 mfit1 | 46.696682406 48.956153307 33.4917773 0.000000000 41.481783789
#> 25 vfit2 mfit2 | 46.696682406 54.820519737 36.4501942 5.564979855 41.756510561
#> 26 vfit3 mfit1 | 46.128908438 48.388379339 33.4917773 0.000000000 30.706638845
#> 27 vfit3 mfit2 | 46.128908438 54.252745770 36.4501942 5.564979855 30.981365617
#> 28 mfit1 mfit2 | 0.000000000 5.864366431 2.9584169 5.564979855 0.274726771
#>
#> $LRT$Df_diff
#> net0 net1 | V1 V2 V3 V4 V5
#> 1 fit0 fit1 | 1 1 1 1 1
#> 2 fit0 fit2 | 5 5 5 5 5
#> 3 vfit1 fit0 | 1 1 0 2 0
#> 4 vfit2 fit0 | 2 2 0 2 2
#> 5 vfit3 fit0 | 1 1 0 2 0
#> 6 fit0 mfit1 | 1 1 3 2 3
#> 7 fit0 mfit2 | 1 2 5 0 4
#> 8 fit1 fit2 | 4 4 4 4 4
#> 9 vfit1 fit1 | 2 2 1 3 1
#> 10 vfit2 fit1 | 3 3 1 3 3
#> 11 vfit3 fit1 | 2 2 1 3 1
#> 12 mfit1 fit1 | 0 2 2 3 2
#> 13 fit1 mfit2 | 0 1 4 1 3
#> 14 vfit1 fit2 | 6 6 5 7 5
#> 15 vfit2 fit2 | 7 7 5 7 7
#> 16 vfit3 fit2 | 6 6 5 7 5
#> 17 mfit1 fit2 | 4 6 2 7 2
#> 18 mfit2 fit2 | 4 3 0 5 1
#> 19 vfit2 vfit1 | 1 1 0 0 2
#> 20 vfit1 vfit3 | 0 0 0 0 0
#> 21 vfit1 mfit1 | 2 0 3 0 3
#> 22 vfit1 mfit2 | 2 3 5 2 4
#> 23 vfit2 vfit3 | 1 1 0 0 2
#> 24 vfit2 mfit1 | 3 1 3 0 5
#> 25 vfit2 mfit2 | 3 4 5 2 6
#> 26 vfit3 mfit1 | 2 0 3 0 3
#> 27 vfit3 mfit2 | 2 3 5 2 4
#> 28 mfit1 mfit2 | 0 3 2 2 1
#>
#> $LRT$pval
#> net0 net1 | V1 V2 V3 V4 V5
#> 1 fit0 fit1 | 0.0000 0.0000 0.5325 0.4740 0.8526
#> 2 fit0 fit2 | 0.0000 0.0000 0.0000 0.2155 0.0000
#> 3 vfit1 fit0 | 0.9384 0.9570 1.0000 0.9954 1.0000
#> 4 vfit2 fit0 | 0.7506 0.7518 1.0000 0.9954 0.0046
#> 5 vfit3 fit0 | 0.9384 0.9570 1.0000 0.9954 1.0000
#> 6 fit0 mfit1 | 0.0000 0.0000 0.0000 0.9954 0.0000
#> 7 fit0 mfit2 | 0.0000 0.0000 0.0000 0.0000 0.0000
#> 8 fit1 fit2 | 0.0003 0.2186 0.0000 0.1613 0.0000
#> 9 vfit1 fit1 | 0.0000 0.0000 0.5325 0.9141 0.8526
#> 10 vfit2 fit1 | 0.0000 0.0000 0.5325 0.9141 0.0128
#> 11 vfit3 fit1 | 0.0000 0.0000 0.5325 0.9141 0.8526
#> 12 mfit1 fit1 | 0.0000 0.6497 0.0000 0.9141 0.0000
#> 13 fit1 mfit2 | 0.0000 0.0253 0.0000 0.0247 0.0000
#> 14 vfit1 fit2 | 0.0000 0.0000 0.0000 0.4208 0.0000
#> 15 vfit2 fit2 | 0.0000 0.0000 0.0000 0.4208 0.0000
#> 16 vfit3 fit2 | 0.0000 0.0000 0.0000 0.4208 0.0000
#> 17 mfit1 fit2 | 0.6802 0.3581 0.2278 0.4208 0.8704
#> 18 mfit2 fit2 | 0.6802 0.8617 1.0000 0.9115 0.9581
#> 19 vfit2 vfit1 | 0.4511 0.4511 1.0000 1.0000 0.0046
#> 20 vfit1 vfit3 | 1.0000 1.0000 1.0000 1.0000 1.0000
#> 21 vfit1 mfit1 | 0.0000 0.0000 0.0000 1.0000 0.0000
#> 22 vfit1 mfit2 | 0.0000 0.0000 0.0000 0.0619 0.0000
#> 23 vfit2 vfit3 | 0.4511 0.4511 1.0000 1.0000 0.0046
#> 24 vfit2 mfit1 | 0.0000 0.0000 0.0000 1.0000 0.0000
#> 25 vfit2 mfit2 | 0.0000 0.0000 0.0000 0.0619 0.0000
#> 26 vfit3 mfit1 | 0.0000 0.0000 0.0000 1.0000 0.0000
#> 27 vfit3 mfit2 | 0.0000 0.0000 0.0000 0.0619 0.0000
#> 28 mfit1 mfit2 | 1.0000 0.1184 0.2278 0.0619 0.6002
#>
#> $LRT$decision
#> net0 net1 | V1 V2 V3 V4 V5
#> 1 fit0 fit1 | fit1 fit1 fit0 fit0 fit0
#> 2 fit0 fit2 | fit2 fit2 fit2 fit0 fit2
#> 3 vfit1 fit0 | vfit1 vfit1 - vfit1 -
#> 4 vfit2 fit0 | vfit2 vfit2 - vfit2 fit0
#> 5 vfit3 fit0 | vfit3 vfit3 - vfit3 -
#> 6 fit0 mfit1 | mfit1 mfit1 mfit1 mfit1 mfit1
#> 7 fit0 mfit2 | mfit2 mfit2 mfit2 mfit2 mfit2
#> 8 fit1 fit2 | fit2 fit1 fit2 fit1 fit2
#> 9 vfit1 fit1 | fit1 fit1 vfit1 vfit1 vfit1
#> 10 vfit2 fit1 | fit1 fit1 vfit2 vfit2 fit1
#> 11 vfit3 fit1 | fit1 fit1 vfit3 vfit3 vfit3
#> 12 mfit1 fit1 | mfit1 mfit1 mfit1 mfit1 mfit1
#> 13 fit1 mfit2 | mfit2 mfit2 mfit2 mfit2 mfit2
#> 14 vfit1 fit2 | fit2 fit2 fit2 vfit1 fit2
#> 15 vfit2 fit2 | fit2 fit2 fit2 vfit2 fit2
#> 16 vfit3 fit2 | fit2 fit2 fit2 vfit3 fit2
#> 17 mfit1 fit2 | mfit1 mfit1 mfit1 mfit1 mfit1
#> 18 mfit2 fit2 | mfit2 mfit2 - mfit2 mfit2
#> 19 vfit2 vfit1 | vfit2 vfit2 - - vfit1
#> 20 vfit1 vfit3 | - - - - -
#> 21 vfit1 mfit1 | mfit1 mfit1 mfit1 - mfit1
#> 22 vfit1 mfit2 | mfit2 mfit2 mfit2 vfit1 mfit2
#> 23 vfit2 vfit3 | vfit2 vfit2 - - vfit3
#> 24 vfit2 mfit1 | mfit1 mfit1 mfit1 - mfit1
#> 25 vfit2 mfit2 | mfit2 mfit2 mfit2 vfit2 mfit2
#> 26 vfit3 mfit1 | mfit1 mfit1 mfit1 - mfit1
#> 27 vfit3 mfit2 | mfit2 mfit2 mfit2 vfit3 mfit2
#> 28 mfit1 mfit2 | - mfit1 mfit1 mfit1 mfit1
#>
#>
#> $counts
#> V1 V2 V3 V4 V5
#> fit0 0 0 1 2 2
#> fit1 4 5 0 1 1
#> fit2 5 4 5 0 5
#> vfit1 1 1 1 4 2
#> vfit2 3 3 1 4 0
#> vfit3 1 1 1 4 2
#> mfit1 6 7 7 4 7
#> mfit2 6 6 5 3 6
#>
#> attr(,"alpha")
#> [1] 0.05
# This does the same thing as above but at the nodewise levelMore examples to be added soon.
Please contact trevorswanson222@gmail.com with any questions.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.