mcIRT (version 0.41)

plot_ldistr: Plot latent distributions

Description

The resulting latent distribution is plotted. This is especially relevant when using a nonparametric estimation of the latent distribution.

Usage

plot_ldistr(x,...)

Arguments

x

An object of class nrm or nelm.

...

Insert further arguments to the plot function.

Details

The function is plotting quadrature nodes and weights for each group (in the case of a multigroup estimation) which are approximating the posterior ability distribution for the group.

See Also

nrm

nelm

plotINF

Examples

## Not run: 
# ## simulating data
# 
# NUMBI <- 5
# ParList <- lapply(1:NUMBI,function(x)
# {
#   Item1 <- c(c(-2,-1,1,2),c(-1.2,0.3,0.2,0.7))
#   names(Item1) <- c(paste("zeta",1:4,sep=""),paste("lamb",1:4,sep=""))
#   Item1
# })
# 
# 
# names(ParList) <- paste("item",1:NUMBI,sep="")
# 
# perp1 <- rnorm(5000,0,1)
# perp2 <- rnorm(5000,1,1)
# 
# simdat1 <- NRM.sim(ParList,perp1)
# simdat2 <- NRM.sim(ParList,perp2)
# 
# simdat1 <- data.frame(ID=1:5000,simdat1)
# simdat2 <- data.frame(ID=5001:10000,simdat2)
# 
# simdatalla <- merge(simdat1,simdat2,all=T)
# simdatall  <- simdatalla[,-1]
# 
# head(simdatall)
# gruAB <- factor(rep(c("A","B"),each=5000))
# 
# DAT1 <- data.frame(simdatall,ABgroup = gruAB)
# 
# head(DAT1)
# 
# ## reshaping the data
# reshOBJ <- reshMG(DAT1,items=1:NUMBI,groups=NUMBI+1,correct=rep(1,NUMBI))
# 
# 
# ## estimating the model
# examp1 <- nrm(reshOBJ, ctrl=list(nodes=31))
# 
# ## End(Not run) 

# loading the estimated model, because estimating the model during example check takes too long

data(examp1)

plot_ldistr(examp1)