imp.mix {mix} | R Documentation |
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 |
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 |
x |
the original data matrix used to create the summary list |
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