Description Usage Arguments Details Value Author(s) References Examples

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.

1 | ```
rsem.pattern(x, print=TRUE)
``` |

`x` |
A matrix as data |

`print` |
Whether to print the missing data pattern. The default is TRUE. |

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.

`x` |
Data ordered according to missing data pattern |

`misinfo` |
Missing data pattern matrix |

`mispat` |
Missing data pattern in better readable form. |

Ke-Hai Yuan and Zhiyong Zhang

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

1 2 3 4 5 6 | ```
#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 May 31, 2017, 1:32 a.m.

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