Preliminary manipulations for a matrix of incomplete continuous data.
Sorts rows of x by missingness patterns, and centers/scales
columns of x. Calculates various bookkeeping quantities needed
for input to other functions, such as em.norm and da.norm.
prelim.norm(x)
x |
data matrix containing missing values. The rows of x
correspond to observational units, and the columns to variables.
Missing values are denoted by |
a list of thirteen components that summarize various features of x after the data have been 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. This vector has names that correspond to the column names of x, if any. |
r |
matrix of response indicators showing the missing data patterns in x. Dimension is (S,p) where S is the number of distinct missingness patterns in the rows of x, and p is the number of columns 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 column names correspond to the column names of x. |
See Section 5.3.1 of Schafer (1996).
data(mdata) s <- prelim.norm(mdata) #do preliminary manipulations s$nmis[s$co] #look at nmis s$r #look at missing data patterns
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