# 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

1 | ```
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.

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

### Value

`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. |

### Author(s)

Zhiyong Zhang and Ke-Hai Yuan

### References

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

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