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
# \donttest{
dat <- expand.table(LSAT7)
(original <- mirt(dat, 1))
#>
#>
#> Call:
#> mirt(data = dat, model = 1)
#>
#> Full-information item factor analysis with 1 factor(s).
#> Converged within 1e-04 tolerance after 28 EM iterations.
#> mirt version: 1.44.5
#> M-step optimizer: BFGS
#> EM acceleration: Ramsay
#> Number of rectangular quadrature: 61
#> Latent density type: Gaussian
#>
#> Log-likelihood = -2658.805
#> Estimated parameters: 10
#> AIC = 5337.61
#> BIC = 5386.688; SABIC = 5354.927
#> G2 (21) = 31.7, p = 0.0628
#> RMSEA = 0.023, CFI = NaN, TLI = NaN
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))
#>
#>
#> Call:
#> mirt(data = dat, model = 1)
#>
#> Full-information item factor analysis with 1 factor(s).
#> Converged within 1e-04 tolerance after 25 EM iterations.
#> mirt version: 1.44.5
#> M-step optimizer: BFGS
#> EM acceleration: Ramsay
#> Number of rectangular quadrature: 61
#> Latent density type: Gaussian
#>
#> Log-likelihood = -2652.1
#> Estimated parameters: 10
#> AIC = 5324.2
#> BIC = 5373.277; SABIC = 5341.517
#>
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))
#>
#>
#> Call:
#> mirt(data = fulldata, model = 1)
#>
#> Full-information item factor analysis with 1 factor(s).
#> Converged within 1e-04 tolerance after 24 EM iterations.
#> mirt version: 1.44.5
#> M-step optimizer: BFGS
#> EM acceleration: Ramsay
#> Number of rectangular quadrature: 61
#> Latent density type: Gaussian
#>
#> Log-likelihood = -2660.331
#> Estimated parameters: 10
#> AIC = 5340.663
#> BIC = 5389.741; SABIC = 5357.98
#> G2 (21) = 30.61, p = 0.0804
#> RMSEA = 0.021, CFI = NaN, TLI = NaN
# 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)
# }