I am trying to run a simple one-dimensional Bayesian IRT-model on a 9
(respondents)*43(items) dataset, but I want to specify separate normal
priors for my item-parameters.
With sample data, I run
data(SupremeCourt)
priors<-matrix(NA,ncol=43,nrow=2)
priors[1,]<-0
priors[2,]<-seq(1,43,1)
posterior3 <- MCMCirtKd(t(SupremeCourt),dimensions=1,
theta.constraints=list(Scalia="+", Ginsburg="-"),
b0=priors,
B0=1000,
burnin=5, mcmc=2000, thin=5, verbose=500,
store.item=TRUE,store.ability=F)
I assume from the documentary that b0 "Can be either a scalar or a matrix
of dimension (K + 1) × items". But if I run this, no matter how I specify
it, I get "l0 neither matrix, list, nor scalar".
It seems like a package-bug to me, but I have no clue how to specify
separate priors for item-parameters.
Thanks, Tobi
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