Description Usage Arguments Details Value Author(s) References

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=FALSE)
``` |

`x` |
A matrix as data |

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

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.

In addition, a matrix of 0/1 is also used to indicate missing data. 1 means missing and 0 means observed.

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

`misinfo` |
Missing data pattern matrix |

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

`y` |
The original data. |

Zhiyong Zhang and Ke-Hai Yuan

Yuan, K.-H., & Zhang, Z. (2012). Robust Structural Equation Modeling with Missing Data and Auxiliary Variables. Psychometrika, 77(4), 803-826.

alpha documentation built on May 31, 2017, 4:38 a.m.

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