simDTM: Simulating data for diagnostic tree model

In GDINA: The Generalized DINA Model Framework

Simulating data for diagnostic tree model

Description

Data generation for diagnostic tree model

Usage

simDTM(N, Qc, gs.parm, Tmatrix, red.delta = NULL, att.gr = NULL)

Arguments

N	sample size
Qc	Association matrix between attributes (column) and PSEUDO items (row); The first column is item number and the second column is the pseudo item number for each item. If a pseudo item has more than one nonzero categories, more than one rows are needed.
gs.parm	the same as the gs.parm in simGDINA function in the GDINA package. It is a list with the same number of elements as the number of rows in the Qc matrix
Tmatrix	mapping matrix showing the relation between the OBSERVED responses (rows) and the PSEDUO items (columns); The first column gives the observed responses.
red.delta	reduced delta parameters using logit link function
att.gr	attribute group indicator

Examples

## Not run: 
K=5
g=0.2
item.no <- rep(1:6,each=4)
# the first node has three response categories: 0, 1 and 2
node.no <- rep(c(1,1,2,3),6)
Q1 <- matrix(0,length(item.no),K)
Q2 <- cbind(7:(7+K-1),rep(1,K),diag(K))
for(j in 1:length(item.no)) {
  Q1[j,sample(1:K,sample(3,1))] <- 1
}
Qc <- rbind(cbind(item.no,node.no,Q1),Q2)
Tmatrix.set <- list(cbind(c(0,1,2,3,3),c(0,1,2,1,2),c(NA,0,NA,1,NA),c(NA,NA,0,NA,1)),
cbind(c(0,1,2,3,4),c(0,1,2,1,2),c(NA,0,NA,1,NA),c(NA,NA,0,NA,1)),
cbind(c(0,1),c(0,1)))
Tmatrix <- Tmatrix.set[c(1,1,1,1,1,1,rep(3,K))]
sim <- simDTM(N=2000,Qc=Qc,gs.parm=matrix(0.2,nrow(Qc),2),Tmatrix=Tmatrix)
est <- DTM(dat=sim$dat,Qc=Qc,Tmatrix = Tmatrix)

## End(Not run)

GDINA documentation built on July 9, 2023, 6:16 p.m.