| prelim.mix {mix} | R Documentation |
This function performs grouping and sorting operations on a mixed
dataset with missing values. It creates a list that is
needed for input to em.mix, da.mix,
imp.mix, etc.
prelim.mix(x, p)
x |
data matrix containing missing values. The rows of x correspond to
observational units, and the columns to variables. Missing values are
denoted by NA. The categorical variables must be in
the first p columns
of x, and they must be coded with consecutive positive integers
starting with 1. For example, a binary variable must be coded as 1,2
rather than 0,1.
|
p |
number of categorical variables in x |
a list of twenty-nine (!) components that summarize various features of x after the data have been collapsed, centered, scaled, and sorted by missingness patterns. Components that might be of interest to the user include:
nmis |
a vector of length ncol(x) containing the number of
missing values for each variable in x.
|
r |
matrix of response indicators showing the missing data patterns in
x.
Observed values are indicated by 1 and missing values by 0. The row
names give the number of observations in each pattern, and the columns
correspond to the columns of x.
|
Schafer, J. L. (1996) Analysis of Incomplete Multivariate Data. Chapman & Hall, Chapter 9.
em.mix, ecm.mix,
da.mix, dabipf.mix, imp.mix,
getparam.mix
data(stlouis) s <- prelim.mix(stlouis, 3) # do preliminary manipulations s$nmis # look at nmis s$r # look at missing data patterns