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28 lines
1.0 KiB
R
28 lines
1.0 KiB
R
library(TAM)
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pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
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}
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if (method=='JML') {
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tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
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}
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if (method!='MML' & method!='JML') {
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stop('Invalid method. Please choose among MML or JML')
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}
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return(tam1)
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}
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dff <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N100/scenario_5A_100.csv')
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dfff <- dff[dff$replication==1,]
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facets <- dfff$TT
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dfff$item4_noTT <- NA
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dfff$item4_TT <- NA
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dfff[dfff$TT==0,]$item4_noTT <- dfff[dfff$TT==0,"item4"]
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dfff[dfff$TT==1,]$item4_TT <- dfff[dfff$TT==1,"item4"]
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mml.mod <- tam.mml(resp=dfff[,c('item1','item2',"item3","item4_noTT",'item4_TT')],Y=dfff$TT,constraint='cases'
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,irtmodel = "PCM2",est.variance = T,verbose=F)
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