Impute missing multivariate normal data
Draws missing elements of a data matrix under the multivariate normal model and a user-supplied parameter
imp.norm(s, theta, x)
s |
summary list of an incomplete normal data matrix |
theta |
value of the normal parameter under which the missing data are to be
randomly imputed. This is a parameter vector in packed storage, such
as one created by |
x |
the original data matrix used to create the summary list |
This function simply performs one I-step of data augmentation.
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.
Before this function may be used, the random number generator seed
must be initialized with rngseed at least once in the current S
session.
See Section 5.4.1 of Schafer (1996).
prelim.norm, makeparam.norm, and rngseed.
data(mdata) s <- prelim.norm(mdata) #do preliminary manipulations thetahat <- em.norm(s) #find the mle rngseed(1234567) #set random number generator seed ximp <- imp.norm(s,thetahat,mdata) #impute missing data under the MLE
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