Compute posterior estimates of random effect

Stochastically compute random effects for MixedClass objects with Metropolis-Hastings samplers and averaging over the draws to obtain expected a posteriori predictions. Returns a list of the estimated effects.

Usage

randef(x, ndraws = 1000, thin = 10, return.draws = FALSE)

Arguments

x: an estimated model object from the mixedmirt function
ndraws: total number of draws to perform. Default is 1000
thin: amount of thinning to apply. Default is to use every 10th draw
return.draws: logical; return a list containing the thinned draws of the posterior?

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29.

Chalmers, R. P. (2015). Extended Mixed-Effects Item Response Models with the MH-RM Algorithm. Journal of Educational Measurement, 52, 200-222. doi:10.1111/jedm.12072 doi:10.18637/jss.v048.i06

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples

if (FALSE) { # \dontrun{
# make an arbitrary groups
covdat <- data.frame(group = rep(paste0('group', 1:49), each=nrow(Science)/49))

# partial credit model
mod <- mixedmirt(Science, covdat, model=1, random = ~ 1|group)
summary(mod)

effects <- randef(mod, ndraws = 2000, thin = 20)
head(effects$Theta)
head(effects$group)

} # }