[R] Imputation method on binary data

[(Ted Harding)]

On 24-Oct-07 08:51:30, sigalit mangut-leiba wrote:
> hello,
> I want to do a single Imputation method on binary data set.
> Is it possible to use imp.cat from CAT package?
> I have a problem defining "theta" when data is binary.
> Do you know any references on the subject?
> Thank you,
> Sigalit.

There should be no problem in principle. However, you must
make sure to do things in the correct sequence.

1. Make sure your data are in a matrix, not a dataframe,
   where the rows correzspond to cases and the columns
   correspond to variables. (In passing: you must of
   course have at least two variables). If your data are
   in a dataframe Y in the first instance, first do

   X <- as.matrix(Y)

2. Make sure that the binary outcomes are coded as 1 and 2,
   not 0 and 1. Missing values should be coded as NA. If
   the outcomes are 0 and 1, then do

   X <- X+1

3. Now do the preliminary step, which creates a specially
   formatted representation of the data, missing value
   patterns, etc. (and of course first load the 'cat' package):

   library(cat)
   s <- prelim.cat(X)

   where X is your data matrix as in (1) and (2).

4. Next, the maximum-likelihood estimation:

   t <- em.cat(s)

5. Now you can embark on as many imputations as you wish,
   alternating between da.cat() and imp.cat():

   t <- da.cat(s,t)
   imp.1 <- imp.cat(s,t)

   t <- da.cat(s,t)
   imp.2 <- imp.cat(s,t)

   ...

More details can be found in the documentation for the
functions listed by

   library(help=cat)

e.g. ?prelim.cat, ?em.cat, ?da.cat, ?imp.cat, etc.

The above is for a straightforward imputation of missing
values in a dataset of caregorical variables, and does not
incorporate any special modelling considerations. See also:
?ecm.cat and other functions.

Hoping this helps,
Ted.

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E-Mail: (Ted Harding) <Ted.Harding at manchester.ac.uk>
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Date: 24-Oct-07                                       Time: 10:59:01
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