Given an estimated model from any of mirt's model fitting functions and an estimate of the
latent trait, impute plausible missing data values. Returns the original data in a
data.frame without any NA values. If a list of Theta values is supplied then a
list of complete datasets is returned instead.
Arguments
- x
an estimated model x from the mirt package
- Theta
a matrix containing the estimates of the latent trait scores (e.g., via
fscores)- warn
logical; print warning messages?
- ...
additional arguments to pass
References
Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06
Author
Phil Chalmers rphilip.chalmers@gmail.com
Examples
if (FALSE) { # \dontrun{
dat <- expand.table(LSAT7)
(original <- mirt(dat, 1))
NAperson <- sample(1:nrow(dat), 20, replace = TRUE)
NAitem <- sample(1:ncol(dat), 20, replace = TRUE)
for(i in 1:20)
dat[NAperson[i], NAitem[i]] <- NA
(mod <- mirt(dat, 1))
scores <- fscores(mod, method = 'MAP')
# re-estimate imputed dataset (good to do this multiple times and average over)
fulldata <- imputeMissing(mod, scores)
(fullmod <- mirt(fulldata, 1))
# with multipleGroup
set.seed(1)
group <- sample(c('group1', 'group2'), 1000, TRUE)
mod2 <- multipleGroup(dat, 1, group, TOL=1e-2)
fs <- fscores(mod2)
fulldata2 <- imputeMissing(mod2, fs)
} # }