| imputeMissing {mirt} | R Documentation |
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
## 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)