Kfind.boxcox {boxcoxmix} | R Documentation |

A grid search over the parameter `K`

, to set the best number of
mass-points.

Kfind.boxcox( formula, groups = 1, data, lambda = 1, EMdev.change = 1e-04, steps = 500, find.k = c(2, 10), model.selection = "aic", start = "gq", find.tol = c(0, 1.5), steps.tol = 15, ... )

`formula` |
a formula describing the transformed response and the fixed effect model (e.g. y ~ x). |

`groups` |
the random effects. To fit overdispersion models , set |

`data` |
a data frame containing variables used in the fixed and random effect models. |

`lambda` |
a transformation parameter, setting |

`EMdev.change` |
a small scalar, with default 0.0001, used to determine when to stop EM algorithm. |

`steps` |
maximum number of iterations for the EM algorithm. |

`find.k` |
search in a range of |

`model.selection` |
Set |

`start` |
a description of the initial values to be used in the fitted model, Quantile-based version "quantile" or Gaussian Quadrature "gq" can be set. |

`find.tol` |
search in a range of |

`steps.tol` |
number of points in the grid search of |

`...` |
extra arguments will be ignored. |

Not only the shape of the distribution causes the skewness it may due to the use of an
insufficient number of classes, `K`

. For this, the `Kfind.boxcox()`

function
was created to search over a selected range of `K`

and find the best. For each number
of classes, a grid search over `tol`

is performed and the `tol`

with the lowest
`aic`

or `bic`

value is considered as the optimal. Having the minimal `aic`

or `bic`

values for a whole range of
`K`

that have been specified beforehand, the `Kfind.boxcox()`

function can find
the best number of the component as the one with the smallest value. It also plots the `aic`

or `bic`

values against
the selected range of `K`

, including a vertical line indicating the best value of `K`

that minimizes the model selection criteria. The full range of
classes and their corresponding optimal `tol`

can be printed off from the `Kfind.boxcox()`

's
output and used with other boxcoxmix functions as starting points.

`MinDisparity` |
the minimum disparity found. |

`Best.K` |
the
value of |

`AllMinDisparities ` |
a vector containing all minimum disparities calculated on the grid. |

`AllMintol ` |
list of |

`All.K ` |
list of |

`All.aic` |
the Akaike information criterion of all fitted regression models. |

`All.bic` |
the Bayesian information criterion of all fitted regression models. |

Amani Almohaimeed and Jochen Einbeck

# Fabric data data(fabric, package = "npmlreg") teststr<-Kfind.boxcox(y ~ x, data = fabric, start = "gq", groups=1, find.k = c(2, 3), model.selection = "aic", steps.tol=5) # Minimal AIC: 202.2114 at K= 2

[Package *boxcoxmix* version 0.28 Index]