person.parameter.rasch.copula.Rd
Ability estimates as maximum likelihood estimates (MLE) are provided by the Rasch copula model.
person.parameter.rasch.copula(raschcopula.object, numdiff.parm=0.001, conv.parm=0.001, maxiter=20, stepwidth=1, print.summary=TRUE, ...)
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 |
A list with following entries
Estimated person parameters
Inflation of individual standard errors due to local dependence
Ability estimates for each unique response pattern
Item response pattern
Summary statistics of person parameter estimates
See rasch.copula2
for estimating Rasch copula models.
############################################################################# # EXAMPLE 1: Reading Data ############################################################################# data(data.read) dat <- data.read # define item cluster itemcluster <- rep( 1:3, each=4 ) mod1 <- sirt::rasch.copula2( dat, itemcluster=itemcluster ) summary(mod1) # person parameter estimation under the Rasch copula model pmod1 <- sirt::person.parameter.rasch.copula(raschcopula.object=mod1 ) ## Mean percentage standard error inflation ## missing.pattern Mperc.seinflat ## 1 1 6.35 if (FALSE) { ############################################################################# # 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 <- sirt::sim.rasch.dep( theta, b, itemcluster, rho ) colnames(dat) <- paste("I", seq(1,ncol(dat)), sep="") # estimate Rasch copula model mod1 <- sirt::rasch.copula2( dat, itemcluster=itemcluster ) summary(mod1) # person parameter estimation under the Rasch copula model pmod1 <- sirt::person.parameter.rasch.copula(raschcopula.object=mod1 ) ## Mean percentage standard error inflation ## missing.pattern Mperc.seinflat ## 1 1 10.48 }