gic: Generalised Information Criterion
In ggmix: Variable Selection in Linear Mixed Models for SNP Data

Description Usage Arguments Details Value References See Also

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

gic(ggmix_fit, ...)

## Default S3 method:
gic(ggmix_fit, ...)

## S3 method for class 'ggmix_fit'
gic(ggmix_fit, ..., an = log(log(n)) * log(p))

`ggmix_fit`	An object of class `ggmix_fit` which is outputted by the `ggmix` function
`...`	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.

ggmix

ggmix documentation built on April 13, 2021, 9:06 a.m.