# NOT RUN {
# Create a sample data set with 3 classes and no covariates
# and run poLCA to recover the specified parameters.
probs <- list(matrix(c(0.6, 0.1, 0.3,
0.6, 0.3, 0.1,
0.3, 0.1, 0.6),
ncol = 3,byrow = TRUE), # conditional resp prob to Y1
matrix(c(0.2, 0.8,
0.7, 0.3,
0.3, 0.7),
ncol = 2, byrow = TRUE), # conditional resp prob to Y2
matrix(c(0.3, 0.6, 0.1,
0.1, 0.3, 0.6,
0.3, 0.6, 0.1),
ncol = 3,byrow = TRUE), # conditional resp prob to Y3
matrix(c(0.1, 0.1, 0.5, 0.3,
0.5, 0.3, 0.1, 0.1,
0.3, 0.1, 0.1, 0.5),
ncol = 4,byrow = TRUE), # conditional resp prob to Y4
matrix(c(0.1, 0.1, 0.8,
0.1, 0.8, 0.1,
0.8, 0.1, 0.1),
ncol = 3,
byrow = TRUE)) # conditional resp prob to Y5
simdat <- poLCA.simdata(N=1000,probs,P=c(0.2,0.3,0.5))
f1 <- cbind(Y1,Y2,Y3,Y4,Y5)~1
lc1 <- poLCA(f1,simdat$dat,nclass=3)
table(lc1$predclass,simdat$trueclass)
# Create a sample dataset with 2 classes and three covariates.
# Then compare predicted class memberships when the model is
# estimated "correctly" with covariates to when it is estimated
# "incorrectly" without covariates.
simdat2 <- poLCA.simdata(N=1000,ndv=7,niv=3,nclass=2,b=matrix(c(1,-2,1,-1)))
f2a <- cbind(Y1,Y2,Y3,Y4,Y5,Y6,Y7)~X1+X2+X3
lc2a <- poLCA(f2a,simdat2$dat,nclass=2)
f2b <- cbind(Y1,Y2,Y3,Y4,Y5,Y6,Y7)~1
lc2b <- poLCA(f2b,simdat2$dat,nclass=2)
table(lc2a$predclass,lc2b$predclass)
# }
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