sirt (version 1.5-0)

rasch.va: Estimation of the Rasch Model with Variational Approximation

Description

This function estimates the Rasch model by the estimation method of variational approximation (Rijmen & Vomlel, 2008).

Usage

rasch.va(dat, globconv = 0.001, maxiter = 1000)

Arguments

dat

Data frame with dichotomous item responses

globconv

Covergence criterion for item parameters

maxiter

Maximal number of iterations

Value

• A list with following entries:
• sig
  Standard deviation of the trait
• item
  Data frame with item parameters
• xsi.ij
  Data frame with variational parameters $\xi_{ij}$
• mu.i
  Vector with individual means $\mu_i$
• sigma2.i
  Vector with individual variances $\sigma_i^2$

References

Rijmen, F., & Vomlel, J. (2008). Assessing the performance of variational methods for mixed logistic regression models. Journal of Statistical Computation and Simulation, 78, 765-779.

Examples

#############################################################################
# SIMULATED EXAMPLE 1: Rasch model
#############################################################################
set.seed(8706)
N <- 5000
I <- 20
dat <- sim.raschtype( rnorm(N,sd=1.3) , b= seq(-2,2,len=I) )

# estimation via variational approximation
mod1 <- rasch.va(dat)

# estimation via marginal maximum likelihood
mod2 <- rasch.mml2(dat)

# estmation via joint maximum likelihood
mod3 <- rasch.jml(dat)

# compare sigma
round( c( mod1$sig , mod2$sd.trait ) , 3 )
## [1] 1.222 1.314

# compare b
round( cbind( mod1$item$b , mod2$item$b , mod3$item$itemdiff) , 3 )
##         [,1]   [,2]   [,3]
##  [1,] -1.898 -1.967 -2.090
##  [2,] -1.776 -1.841 -1.954
##  [3,] -1.561 -1.618 -1.715
##  [4,] -1.326 -1.375 -1.455
##  [5,] -1.121 -1.163 -1.228