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