imputeMissing {mirt}

R Documentation

Imputing plausible data for missing values

Description

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.

Usage

imputeMissing(x, Theta, warn = TRUE, ...)

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

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

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

Examples

## No test: 
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: 1.43 
## 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 39 EM iterations.
## mirt version: 1.43 
## M-step optimizer: BFGS 
## EM acceleration: Ramsay 
## Number of rectangular quadrature: 61
## Latent density type: Gaussian 
## 
## Log-likelihood = -2651.701
## Estimated parameters: 10 
## AIC = 5323.401
## BIC = 5372.479; SABIC = 5340.718

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 25 EM iterations.
## mirt version: 1.43 
## M-step optimizer: BFGS 
## EM acceleration: Ramsay 
## Number of rectangular quadrature: 61
## Latent density type: Gaussian 
## 
## Log-likelihood = -2661.213
## Estimated parameters: 10 
## AIC = 5342.426
## BIC = 5391.503; SABIC = 5359.743
## G2 (21) = 33.03, p = 0.0459
## RMSEA = 0.024, 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)

## End(No test)

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