| gic {ggmix} | R Documentation |
Generalised Information Criterion
Description
Calculates the generalised information criterion for each value of the tuning parameter lambda
Usage
gic(ggmix_fit, ...)
## Default S3 method:
gic(ggmix_fit, ...)
## S3 method for class 'ggmix_fit'
gic(ggmix_fit, ..., an = log(log(n)) * log(p))
Arguments
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 |
Details
the generalised information criterion used for gaussian response is given by
-2 * loglikelihood(\hat{\Theta}) + an * df
where df is the number of non-zero estimated parameters, including variance components
Value
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
References
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.