Plots the Generalised Information Criteria curve, as a function of the lambda values used

# S3 method for ggmix_gic
plot(
x,
...,
sign.lambda = 1,
type = c("gic", "QQranef", "QQresid", "predicted", "Tukey-Anscombe"),
s = "lambda.min",
newy,
newx
)

plotGIC(x, sign.lambda, lambda.min, ...)

## Arguments

x fitted linear mixed model object of class ggmix_gic from the gic function Other graphical parameters to plot Either plot against log(lambda) (default) or its negative if sign.lambda=-1 gic returns a plot of the GIC vs. log(lambda). QQranef return a qqplot of the random effects. QQresid returns a qqplot of the residuals which is $$y - X\beta - b_i$$ where b_i is the subject specific random effect. predicted returns a plot of the predicted response ($$X \beta$$ + b_i) vs. the observed response, where b_i is the subject specific random effect. Tukey-Anscombe returns a plot of the residuals vs. fitted values ($$X \beta$$) Value of the penalty parameter lambda at which predictions are required. Default is the value s="lambda.min". If s is numeric, it is taken as the value of lambda to be used. Must be a single value of the penalty parameter lambda at which coefficients will be extracted via the coef method for objects of class ggmix_gic. If more than one is supplied, only the first one will be used. the response variable that was provided to ggmix. this is only required for type="QQresis", type="Tukey-Anscombe" and type="predicted" matrix of values for x at which predictions are to be made. Do not include the intercept. this is only required for type="QQresis", type="Tukey-Anscombe" and type="predicted" the value of lambda which minimizes the gic

## Value

plot depends on the type selected

## Details

A plot is produced, and nothing is returned.

gic

## Examples

data("admixed")
fit <- ggmix(x = admixed$xtrain, y = admixed$ytrain,
plot(hdbic)