Calculating IMV values

Here is a slightly more complex example that takes advantage of irw to easily fetch a dataset and to then compute the InterModel Vigorish contrasting predictings for the 2PL to predictions from the 1PL for an example dataset (using cross-validation across 4 folds; see also the documentation in the related imv package). Note the irw_long2resp function which is helpful for reformatting IRW data from long to wide.

Code

df<-irw::irw_fetch("gilbert_meta_2")  #https://github.com/itemresponsewarehouse/Rpkg
resp<-irw::irw_long2resp(df)
resp$id<-NULL
##1pl/Rasch model
m0<-mirt::mirt(resp,1,'Rasch',verbose=FALSE)
##2pl
ni<-ncol(resp)
s<-paste("F=1-",ni,"
             PRIOR = (1-",ni,", a1, lnorm, 0.0, 1.0)",sep="")
model<-mirt::mirt.model(s)
m1<-mirt::mirt(resp,model,itemtype=rep("2PL",ni),method="EM",technical=list(NCYCLES=10000),verbose=FALSE)
##compute IMV comparing predictions from 1pl and 2pl
set.seed(8675309)
omega<-imv::imv.mirt(m0,m1) #https://github.com/intermodelvigorish/imv_R
mean(omega)

[1] 0.01276902