rsem (version 0.5.1)

rsem.pattern: Obtaining missing data patterns

Description

This function obtains the missing data patterns and the number of cases in each patterns. It also tells the number of observed variables and their indices for each pattern.

Usage

rsem.pattern(x, print=FALSE)

Value

x

Data ordered according to missing data pattern

misinfo

Missing data pattern matrix

mispat

Missing data pattern in better readable form.

Arguments

x

A matrix as data

print

Whether to print the missing data pattern. The default is FALSE.

Author

Ke-Hai Yuan and Zhiyong Zhang

Details

The missing data pattern matrix has 2+p columns. The first column is the number cases in that pattern. The second column is the number of observed variables. The last p columns are a matrix with 1 denoting observed data and 0 denoting missing data.

References

Ke-Hai Yuan and Zhiyong Zhang (2011) Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

Examples

#dset<-read.table('MardiaMV25.dat.txt', na.string='-99')  
#dset<-data.matrix(dset)                                  
#n<-dim(dset)[1]
#p<-dim(dset)[2]
#miss_pattern<-rsem.pattern(n,p,dset)
#miss_pattern