sirt (version 1.14-0)

person.parameter.rasch.copula: Person Parameter Estimation of the Rasch Copula Model (Braeken, 2011)

Description

Ability estimates as maximum likelihood estimates (MLE) are provided by the Rasch copula model.

Usage

person.parameter.rasch.copula(raschcopula.object, numdiff.parm = 0.001,  conv.parm = 0.001, maxiter = 20, stepwidth = 1,  print.summary = TRUE, ...)

Arguments

raschcopula.object

Object which is generated by the coderasch.copula2 function.

numdiff.parm

Parameter $h$ for numerical differentiation

conv.parm

Convergence criterion

maxiter

Maximum number of iterations

stepwidth

Maximal increment in iterations

print.summary

Print summary?

...

Further arguments to be passed

Value

A list with following entries A list with following entries

See Also

See rasch.copula2 for estimating Rasch copula models.

Examples

#############################################################################
# EXAMPLE 1: Reading Data
#############################################################################

data(data.read)
dat <- data.read

# define item cluster
itemcluster <- rep( 1:3 , each = 4 )
mod1 <- rasch.copula2( dat , itemcluster = itemcluster )
summary(mod1)

# person parameter estimation under the Rasch copula model
pmod1 <- person.parameter.rasch.copula(raschcopula.object = mod1 )
## Mean percentage standard error inflation
##   missing.pattern Mperc.seinflat
## 1               1           6.35

## Not run: 
# #############################################################################
# # EXAMPLE 2: 12 items nested within 3 item clusters (testlets)
# #   Cluster 1 -> Items 1-4; Cluster 2 -> Items 6-9;  Cluster 3 -> Items 10-12
# #############################################################################
# 
# set.seed(967)
# I <- 12                             # number of items
# n <- 450                            # number of persons
# b <- seq(-2,2, len=I)               # item difficulties
# b <- sample(b)                      # sample item difficulties
# theta <- stats::rnorm( n , sd = 1 ) # person abilities
# # itemcluster
# itemcluster <- rep(0,I)
# itemcluster[ 1:4 ] <- 1
# itemcluster[ 6:9 ] <- 2
# itemcluster[ 10:12 ] <- 3
# # residual correlations
# rho <- c( .35 , .25 , .30 )
# 
# # simulate data
# dat <- sim.rasch.dep( theta , b , itemcluster , rho )
# colnames(dat) <- paste("I" , seq(1,ncol(dat)) , sep="")
# 
# # estimate Rasch copula model 
# mod1 <- rasch.copula2( dat , itemcluster = itemcluster )
# summary(mod1)
# 
# # person parameter estimation under the Rasch copula model
# pmod1 <- person.parameter.rasch.copula(raschcopula.object = mod1 )
#   ## Mean percentage standard error inflation
#   ##   missing.pattern Mperc.seinflat
#   ## 1               1          10.48 
# ## End(Not run)