Imputing plausible data for missing values
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.
imputeMissing(x, Theta, warn = TRUE, ...)
x |
an estimated model x from the mirt package |
Theta |
a matrix containing the estimates of the latent trait scores
(e.g., via |
warn |
logical; print warning messages? |
... |
additional arguments to pass |
Phil Chalmers rphilip.chalmers@gmail.com
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
## Not run:
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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.