IRfactorsim: Simulation for factor copulas for ordinal item response data
In vincenzocoia/CopulaModel: CopulaModel: Dependence Modeling with Copulas

Description Usage Arguments Details Value References See Also Examples

Simulation for factor copulas for ordinal item response data, all bivariate linking copulas in same parametric family for each factor

1 2	sim1irfact(ucuts,n,parobj1,qcond1,copname1,ivect=F) sim2irfact(ucuts,n,parobj1,parobj2,qcond1,qcond2,copname1="",copname2="",ivect=F)

`ucuts`	(ncat-1)xd matrix of cutpoints, increasing in each column and bounded in (0,1); ncat=number of categories
`n`	sample size
`parobj1`	parameter vector of dimension d or parameter matrix with d rows for factor 1, where d is dimension of factor copula; this is dx2 for something like BB1 copula
`parobj2`	parameter vector of dimension d or parameter matrix with d rows for factor 2
`qcond1`	function for copula conditional inverse cdf C_{U\|V}^{-1}(u\|v), choices include qcondfrk, qcondgum, qcondgumr, qcondbb1, qcondbvtcop with fixed nu1.
`qcond2`	function for copula conditional inverse cdf C_{U\|V}^{-1}(u\|v) for second factor, choices include qcondfrk, qcondgum, qcondgumr, qcondbvtcop with fixed nu2.
`copname1`	copula name: the function checks on "frank", "mtcj", "mtcjr", "fgm" for which qcond has closed form.
`copname2`	copula name for factor 2
`ivect`	flag that is T if qcond1 and qcond2 have vectorized forms

These interface to sim1fact and sim1fact, with discretization based on ucuts.

data matrix of dimension nxd with values in 0,...,ncat-1

Nikoloulopoulos A K and Joe H (2015). Factor copula models for item response data, Psychometrika, 80, 126-150.

IRfactormle factorcopsim

 
ucuts=matrix(c(.3,.6,.4,.7,.5,.8),2,3)
param=c(5.5,6.5,4)
set.seed(123)
ydat=sim1irfact(ucuts,n=1000,param,qcond1=qcondfrk,copname1="frank")
print(cor(ydat))
for(j in 1:length(param)) print(table(ydat[,j]))
ydat2=sim2irfact(ucuts,n=1000,param,c(2,2,2),qcond1=qcondgum,qcond2=qcondgum,ivect=TRUE)
print(cor(ydat2))
for(j in 1:length(param)) print(table(ydat2[,j]))