rsem.pattern: Obtaining missing data patterns

In rsem: Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

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)

Arguments

x	A matrix as data
print	Whether to print the missing data pattern. The default is FALSE.

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.

Value

x	Data ordered according to missing data pattern
misinfo	Missing data pattern matrix
mispat	Missing data pattern in better readable form.

Author(s)

Ke-Hai Yuan and Zhiyong Zhang

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

rsem documentation built on Aug. 27, 2023, 1:06 a.m.