Description Usage Arguments Details Value Author(s)

Bootstrap for EM

1 2 3 | ```
bmem.em.boot(x, ram, indirect, v, robust = FALSE,
varphi = 0.1, st= "i", boot = 1000,
moment = FALSE, max_it = 500, ...)
``` |

`x` |
A data set |

`ram` |
RAM path for the mediaiton model |

`indirect` |
A vector of indirect effec |

`v` |
Indices of variables used in the mediation model. If omitted, all variables are used. |

`robust` |
Roubst method |

`varphi` |
Percent of data to be downweighted |

`st` |
Starting values |

`boot` |
Number of bootstraps. Default is 1000. |

`moment` |
Select mean structure or covariance analysis. moment=FALSE, covariance analysis. moment=TRUE, mean and covariance analysis. |

`max_it` |
Maximum number of iterations in EM |

`...` |
Other options for |

The indirect effect can be specified using equations such as `a*b`

, `a*b+c`

, and `a*b*c+d*e+f`

. A vector of indirect effects can be used `indirect=c('a*b', 'a*b+c')`

.

`par.boot` |
Parameter estimates from bootstrap samples |

`par0` |
Parameter estimates from the orignal samples |

Zhiyong Zhang and Lijuan Wang

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