Here is a slightly more complex example showing how to compute the InterModel Vigorish contrasting predictings for the 2PL to predictions from the 1PL for an example dataset (using cross-validation across 4 folds).
library(mirt)
library(irw)
dataset <- redivis::user("datapages")$dataset("item_response_warehouse")
df <- dataset$table("gilbert_meta_2")$to_data_frame()
items<-unique(df$item)
if (all(items %in% 1:length(items))) {
df$item<-paste("item_",df$item,sep='')
items<-unique(df$item)
}
resp<-irw::long2resp(df)
id<-resp$id
resp$id<-NULL
##cross-validation for models estimated in mirt
set.seed(8675309)
ntimes<-4
df$gr<-sample(1:ntimes,nrow(df),replace=TRUE)
x.hold<-df
omega<-numeric()
for (i in 1:ntimes) {
x<-x.hold
x$oos<-ifelse(x$gr==i,1,0)
x0<-x[x$oos==0,]
resp0<-data.frame(irw::long2resp(x0))
id<-resp0$id
resp0$id<-NULL
##rasch model
m0<-mirt(resp0,1,'Rasch',verbose=FALSE)
##2pl
ni<-ncol(resp0)
s<-paste("F=1-",ni,"
PRIOR = (1-",ni,", a1, lnorm, 0.0, 1.0)",sep="")
model<-mirt.model(s)
m1<-mirt(resp0,model,itemtype=rep("2PL",ni),method="EM",technical=list(NCYCLES=10000),verbose=FALSE)
##
z0<-getp(m0,x=x[x$oos==1,],id=id)
z1<-getp(m1,x=x[x$oos==1,],id=id)
z0<-z0[,c("item","id","resp","p")]
names(z0)[4]<-'p1'
z1<-z1[,c("item","id","p")]
names(z1)[3]<-'p2'
z<-merge(z0,z1)
omega[i]<-imv(z,p1="p1",p2="p2")
}
mean(omega)
Here is a slightly more complex example showing how to compute the InterModel Vigorish contrasting predictings for the 2PL to predictions from the 1PL for an example dataset (using cross-validation across 4 folds).