Created
September 1, 2019 03:44
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library("parSim") | |
parSim( | |
### SIMULATION CONDITIONS | |
# Vary sample size: | |
sampleSize = c(250, 500, 1000), | |
# Vary missingness: | |
missing = c(0, 0.1, 0.25), | |
reps = 100, # 5 repititions | |
write = TRUE, # Writing results to a file | |
name = "missingdata_sims_2", # Name of the file | |
nCores = 8, # I use 8 cores here | |
expression = { | |
### SIMULATION CODE ### | |
# Packages: | |
library("psychonetrics") | |
library("dplyr") | |
library("bootnet") | |
library("mice") | |
# Function needed: | |
source("compareNetworks.R") | |
# Generate true network: | |
trueNet <- genGGM(10, nei = 2, propPositive = 0.8, p = 0.25) | |
# Generate data: | |
generator <- ggmGenerator() | |
Data <- generator(sampleSize, trueNet) | |
# Add missings: | |
for (i in 1:ncol(Data)){ | |
Data[runif(sampleSize) < missing,i] <- NA | |
} | |
## Method 1: psychonetrics with FIML | |
# Estimate model (FDR prune): | |
mod <- ggm(Data, estimator = "FIML") %>% | |
runmodel %>% | |
prune(adjust = "fdr", alpha = 0.05, recursive = FALSE) | |
# Extract network: | |
estNet <- getmatrix(mod, "omega") | |
# Compare to true network: | |
results_psychonetrics <- compareNetworks(trueNet, estNet) | |
# Add method: | |
results_psychonetrics$method <- "psychonetrics_FIML" | |
## Method 2: ggmModSelect with mice imputation | |
# Impute data using mice: | |
miceres <- mice(Data, m = 1) | |
imputedData <- complete(miceres) | |
# Run ggmModSelect: | |
net <- estimateNetwork(imputedData, default = "ggmModSelect") | |
estNet <- net$graph | |
# Compare to true network: | |
results_ggmModSelect <- compareNetworks(trueNet, estNet) | |
# Add method: | |
results_ggmModSelect$method <- "ggmModSelect_mice" | |
# Combine both in a data frame: | |
results <- bind_rows(results_psychonetrics,results_ggmModSelect) | |
# Return: | |
results | |
} | |
) |
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