Simulating data for diagnostic tree model
Data generation for diagnostic tree model
simDTM(N, Qc, gs.parm, Tmatrix, red.delta = NULL, att.gr = NULL)
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 |
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.