Description Usage Arguments Details Value Author(s) References See Also Examples

This function estimates fixed effects and predicts random effects in two- and three-level random effects nested models using three rank-based fittings (GR, GEER, JR) via the prediction method algorithm RPP.

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`f` |
An object of class formula describing the mixed effects model. The syntax is same as in the lme4 package. Example: y ~ 1 + sex + age + (1 | region) + (1 | region:school) - sex and age are the fixed effects, region and school are the nested random effects, school is nested within region. |

`data` |
The dataframe to analyze. Data should be cleaned prior to analysis: cluster and subcluster columns are expected to be integers and in order (e.g. all clusters and subclusters ) |

`method` |
string indicating the method to use (one of "gr", "jr", "reml", and "geer"). defaults to "gr". |

`print` |
Whether or not to print a summary of results. Defaults to false. |

`na.omit` |
Whether or not to omit rows containing NA values. Defaults to true. |

`weight` |
When weight="hbr", it uses hbr weights in GEE weights. By default, ="wil", it uses Wilcoxon weights. See the theory in the references. |

`rprpair` |
By default, it uses "hl-disp" in the random prediction procedure (RPP). Also, "med-mad" would be an alternative. |

`verbose` |
Boolean indicating whether to print out diagnostic messages. |

The iterative methods GR and GEER can be quite slow for large datasets; try JR for faster analysis. If you want to use the GR method, try using rprpair='med-mad'. This method avoids building a NxN covariance matrix which can quickly become unwieldly with large data.

The function returns a list of class "rlme". Use summary.rlme to see a summary of the fit.

`formula` |
The model formula. |

`method` |
The method used. |

`fixed.effects` |
Estimate of fixed effects. |

`random.effects` |
Estimate of random effects. |

`standard.residual` |
Residuals. |

`intra.class.correlations` |
Intra/inter-class correlationa estimates obtained from RPP. |

`t.value` |
t-values. |

`p.value` |
p-values. |

`location` |
Location. |

`scale` |
Scale. |

`y` |
The response variable y. |

`num.obs` |
Number of observations in provided dataset. |

`num.clusters` |
The number of clusters. |

`num.subclusters` |
The number of subclusters. |

`effect.err` |
Effect from error. |

`effect.cluster` |
Effect from cluster. |

`effect.subcluster` |
Effect from subcluster. |

`var.b` |
Variances of fixed effects estimate (Beta estimates). |

`xstar` |
Weighted design matrix with error covariance matrix. |

`ystar` |
Weighted response vector with its covariance matrix. |

`ehat` |
The raw residual. |

`ehats` |
The raw residual after weighted step. Scaled residual. |

Yusuf Bilgic [email protected] and Herb Susmann [email protected]

Y. K. Bilgic. Rank-based estimation and prediction for mixed effects models in nested designs. 2012. URL http://scholarworks.wmich.edu/dissertations/40. Dissertation.

T. P. Hettmansperger and J. W. McKean. Robust Nonparametric Statistical Methods. Chapman Hall, 2012.

summary.rlme, plot.rlme, compare.fits

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