Description Usage Arguments Details Value See Also Examples

Estimate the regression error variance function nonparametrically from a partially linear mixed effects model fitted using the model fitting function `plmm`

, and refit the model applying the weighted least squares procedure. `wplmm`

returns an object of the ‘wplmm’ class.

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`object` |
a model fitted with |

`heteroX` |
at most two variables conditioning the heteroskedasticity of the regression error variance. If there are two variables, they can be passed either as a 2-element list or a 2-column matrix. |

`data` |
an optional data frame containing the variables in the model. If relevant variables are not found in data, they are taken from the environment from which |

`nonpar.bws` |
the bandwidth selection method for the kernel regression of the nonparametric component. The default method “h.select” (cross validation (CV) using binning technique), “hcv” (ordinary CV), “GCV” (generalized CV) and “GCV.c” (generalized CV for correlated data) are available. |

`poly.index` |
the degrees of polynomial for the kernel regression of the nonparametric component: either 0 for local constant or 1 (default) for local linear. |

`var.fun.bws` |
the bandwidth selection method for kernel regression of the variance function. A rule-of-thumb type method “ROT” (default), “h.select” (cross validation using binning technique) and “hcv” (ordinary cross validation) are available. |

`var.fun.poly.index` |
the degree of polynomial of the kernel regression to estimate the nonparametric variance function: either 0 (default) for local constant or 1 for local linear. |

`scale.h` |
a scalar or 2-dimensional vector to scale the bandwidths selected for kernel regression of the nonparametric component. The default is 1. When a scalar is given for a nonparametric component of two covariates, it scales the bandwidths in both directions by the same factor. |

`trim` |
if estimated variance function values are below the value of |

`lim.binning` |
the smallest sample size below which binning techniques are not used to calculate the degrees of freedom of the estimated nonparametric component. Then, the ordinary cross-validation is used instead. This option doesn't apply if “GCV.c” is used for |

`...` |
optional arguments relevant to |

There are three methods to select bandwidths for kernel regression of the nonparametric variance function: “h.select” and “hcv” call `h.select`

and `hcv`

, respectively, which are functions of the sm package; “ROT” selects the bandwidths for heteroskedasticity conditioning variable *w* by *sd(w)N^{-1/(4+q)}* where *q* is the number of the conditioning variables (1 or 2) and *N* is the sample size.

`coefficients` |
estimated regression coefficients. |

`fitted.values` |
conditional predictions of the response, defined as the sum of the estimated fixed components and the predicted random intercepts. |

`residuals` |
residuals of the fitted model, defined as the response values minus the conditional predictions of the response. |

`var.comp` |
variance component estimates. |

`nonpar.values` |
estimated function values of the nonparametric component at the data points. |

`h.nonpar` |
the bandwidths used to estimate the nonparametric component. |

`var.fun.values` |
estimated variance function values. Original computations less than the value of |

`h.var.fun` |
the bandwidths used to estimate the nonparametric variance function. |

`rank` |
the degrees of freedom of the parametric component, which doesn't include the intercept term. |

`df.residual` |
the residual degrees of freedom defined as |

`nbins` |
the number of bins (which would have been) used for binning for CV and the calculation of the degrees of freedom. |

`formula` |
formula passed to |

`call` |
the matched call to |

`h0.call` |
the matched call to |

`plmm.call` |
the matched call to the |

`xlevels` |
if there are factors among the covariates in the parametric component, the levels of those factors. |

`heteroX` |
the names of the heteroskedasticity conditioning variables. |

`plmm`

, `h.select`

, `hcv`

, `sm.options`

.

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