Description Usage Arguments Details Value See Also Examples

Select the bandwidths for kernel regression to reduce the partially linear mixed effects model to a mixed effects model.

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`formula` |
a symbolic description of the model to fit with the model fitting function |

`data` |
an optional data frame containing the variables in the |

`nonpar.bws` |
the cross validation method for bandwidth selection. The method is either the default “h.select” (cross validation using binning technique) or “hcv” (ordinary cross validation). |

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

`...` |
optional arguments relevant to |

`select.h0`

yields a list object that can be used for the argument `h0`

in the model fitting function `plmm`

. Bandwidths are selected for kernel regression of the response and the covariates in the fixed parametric component. “h.select” uses binning techniques for cross validation. The number of bins for binning is set to the default integer, the rounded value of `8*log(N)/d)`

, where `N`

is the sample size and `d`

is the number of covariates in the nonparametric component. When the sample size is small (`N < 100`

), binning techniques are not used and the bandwidths selected will coincide with those obtained with “hcv”.

`h0` |
a vector (if the nonparametric component is a function of one variable) or a matrix (if it is a function of two variables) of bandwidths selected. |

`nbins` |
the number of bins (which would be) used for binning. |

`h0.call` |
the matched call to |

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