plot.ggmix_gic {ggmix} | R Documentation |
Plot the Generalised Information Criteria curve produced by gic
Description
Plots the Generalised Information Criteria curve, as a function
of the lambda values used
Usage
## S3 method for class '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
|
sign.lambda |
Either plot against log(lambda) (default) or its negative
if sign.lambda=-1
|
type |
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 )
|
s |
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.
|
newy |
the response variable that was provided to ggmix . this is
only required for type="QQresis" , type="Tukey-Anscombe" and
type="predicted"
|
newx |
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"
|
lambda.min |
the value of lambda which minimizes the gic
|
Details
A plot is produced, and nothing is returned.
Value
plot depends on the type selected
See Also
gic
Examples
data("admixed")
fit <- ggmix(x = admixed$xtrain,
y = admixed$ytrain,
kinship = admixed$kin_train)
hdbic <- gic(fit)
# plot solution path
plot(fit)
# plot HDBIC curve as a function of lambda
plot(hdbic)
[Package
ggmix version 0.0.2
Index]