imp.mix: Impute Missing Data Under General Location Model

In mix: Estimation/Multiple Imputation for Mixed Categorical and Continuous Data

Impute Missing Data Under General Location Model

Description

This function, when used with da.mix or dabipf.mix, can be used to create proper multiple imputations of missing data under the general location model with or without restrictions.

Usage

imp.mix(s, theta, x)

Arguments

s	summary list of an incomplete data matrix x created by the function prelim.mix.
theta	value of the parameter under which the missing data are to be randomly imputed. This is a parameter list such as one created by da.mix or dabipf.mix.
x	the original data matrix used to create the summary list s. If this argument is not supplied, then the data matrix returned by this function may disagree slightly with the observed values in x due to rounding errors.

Details

This function is essentially the I-step of data augmentation.

Value

a matrix of the same form as x, but with all missing values filled in with simulated values drawn from their predictive distribution given the observed data and the specified parameter.

Note

The random number generator seed must be set at least once by the function rngseed before this function can be used.

References

Schafer, J. L. (1996) Analysis of Incomplete Multivariate Data. Chapman & Hall, Chapter 9.

See Also

prelim.mix, da.mix, dabipf.mix, rngseed

Examples

data(stlouis)
s <- prelim.mix(stlouis,3)    # do preliminary manipulations
thetahat <- em.mix(s)   # ML estimate for unrestricted model
rngseed(1234567)     # set random number generator seed
newtheta <- da.mix(s,thetahat,steps=100) # data augmentation
ximp <- imp.mix(s, newtheta, stlouis)  # impute under newtheta

mix documentation built on May 31, 2022, 5:07 p.m.