Description Usage Arguments Details Value References See Also

Calculates the generalised information criterion for each value of the tuning parameter lambda

1 2 3 4 5 6 7 |

`ggmix_fit` |
An object of class |

`...` |
other parameters. currently ignored. |

`an` |
numeric, the penalty per parameter to be used; the default is an = log(log(n))*log(p) where n is the number of subjects and p is the number of parameters |

the generalised information criterion used for gaussian response is given by

*-2 * loglikelihood(\hat{Θ}) + an * df*

where df is the number of non-zero estimated parameters, including variance components

an object with S3 class `"ggmix_gic"`

, `"ggmix_fit"`

,
`"*"`

and `"**"`

where `"*"`

is "lasso" or "gglasso" and
`"**"`

is fullrank or lowrank. Results are provided for converged
values of lambda only.

- ggmix_fit
the ggmix_fit object

- lambda
the sequence of converged tuning parameters

- nzero
the number of non-zero estimated coefficients including the 2 variance parameters which are not penalized and therefore always included

- gic
gic value. a numeric vector with length equal to

`length(lambda)`

- lambda.min.name
a character corresponding to the name of the tuning parameter lambda which minimizes the gic

- lambda.min
the value of lambda which minimizes the gic

Fan Y, Tang CY. Tuning parameter selection in high dimensional penalized likelihood. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2013 Jun 1;75(3):531-52.

Nishii R. Asymptotic properties of criteria for selection of variables in multiple regression. The Annals of Statistics. 1984;12(2):758-65.

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