Note: the estimation process can be time consuming depending on the computing power. You can same some time by reducing the length of the chains.
library(LAWBL)
dat <- sim24ccfa21$dat
head(dat)
R> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
R> [1,] 2 1 2 2 2 2 1 2 1 1 1 1 1 1
R> [2,] 2 1 2 1 2 2 2 2 2 2 2 2 2 1
R> [3,] 1 1 1 1 1 1 1 1 2 2 1 1 2 2
R> [4,] 1 1 1 1 1 1 1 1 1 1 1 1 2 2
R> [5,] 1 1 1 2 1 1 1 1 2 1 1 2 1 1
R> [6,] 1 2 2 2 2 1 2 1 2 1 1 1 1 1
R> [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
R> [1,] 1 1 2 2 1 1 1 1 1 1
R> [2,] 1 1 2 2 2 2 2 2 1 2
R> [3,] 2 1 1 1 1 2 1 1 2 1
R> [4,] 2 2 1 1 2 2 2 1 2 2
R> [5,] 1 1 1 1 1 1 1 1 1 1
R> [6,] 1 1 1 2 1 1 1 1 1 1
J <- ncol(dat) # no. of items
qlam <- sim24ccfa21$qlam
qlam
R> [,1] [,2] [,3] [,4]
R> [1,] 0.7 0.0 0.0 0.00
R> [2,] 0.7 0.0 0.0 0.00
R> [3,] 0.7 0.0 0.0 0.00
R> [4,] 0.7 0.0 0.0 0.00
R> [5,] 0.7 0.0 0.0 0.00
R> [6,] 0.7 0.0 0.0 0.00
R> [7,] 0.7 0.0 0.0 0.00
R> [8,] 0.7 0.0 0.0 0.00
R> [9,] 0.0 0.7 0.0 0.00
R> [10,] 0.0 0.7 0.0 0.00
R> [11,] 0.0 0.7 0.0 0.00
R> [12,] 0.0 0.7 0.0 0.00
R> [13,] 0.0 0.7 0.0 0.00
R> [14,] 0.0 0.7 0.0 0.00
R> [15,] 0.0 0.7 0.0 0.55
R> [16,] 0.0 0.7 0.0 0.55
R> [17,] 0.0 0.0 0.7 0.00
R> [18,] 0.0 0.0 0.7 0.00
R> [19,] 0.0 0.0 0.7 0.00
R> [20,] 0.0 0.0 0.7 0.00
R> [21,] 0.0 0.0 0.7 0.00
R> [22,] 0.0 0.0 0.7 0.00
R> [23,] 0.0 0.0 0.7 0.55
R> [24,] 0.0 0.0 0.7 0.55
K <- ncol(qlam) # no. of factors
ipf <- 8 Q<-matrix(-1,J,K-1); # -1 for unspecified items Q[1:8,1]<-Q[9:16,2]<-Q[17:24,3]<-1 Q m0<-pcirm(dat = dat,Q= Q,LD = TRUE,cati = -1,burn = 2000,iter = 2000,verbose = TRUE) summary(m0) summary(m0, what = 'qlambda') summary(m0, what = 'offpsx') #summarize significant LD terms summary(m0,what='int') summary(m0,what='eigen') #plotting factorial eigenvalue plot_eigen(m0) # trace plot_eigen(m0, what='density') #density plot_eigen(m0, what='APSR') #adj, PSRF
Q<-cbind(Q,-1); Q[c(15:16),K] <- 1 m1<-pcirm(dat = dat,Q= Q,LD = FALSE, cati = -1,burn = 4000,iter = 4000,verbose = TRUE) summary(m1) summary(m1, what = 'qlambda') #close to qlam
tmp<-summary(m1, what="qlambda") Q<-matrix(-1,J,K) Q[tmp!=0]<-1 Q m2<-pcirm(dat = dat,Q= Q,LD = TRUE,cati = -1,burn = 4000,iter = 4000,verbose = TRUE) summary(m2) summary(m2, what = 'qlambda') summary(m2, what = 'offpsx') #summarize significant LD terms summary(m2,what='int') summary(m2,what='eigen') #plotting factorial eigenvalue plot_eigen(m2) # trace plot_eigen(m2, what='density') #density plot_eigen(m2, what='APSR') #adj, PSRF