eNetXplorer (version 1.1.2)

plotLambdaVsQF: generates plot of quality function across lambda

Description

Given alpha, this function generates a plot of the quality (objective) function across lambda, which is useful to examine how was the "best lambda" value selected.

Usage

plotLambdaVsQF(x, alpha.index=NULL, xlab="lambda", 
ylab="QF (response vs out-of-bag predicted)", cex.lab=0.95, main=NULL, 
col.main="black", cex.main=0.95, log="x", type="b", …)

Arguments

x

eNetXplorer object.

alpha.index

Integer indices to select alpha values. Default is 1:length(alpha)

xlab

Custom x-axis label.

ylab

Custom y-axis label.

cex.lab

Axis label size.

main

Custom title.

col.main

Title color.

cex.main

Title size.

log

Log scale axis.

type

Plot type.

…

Additional plotting parameters.

Details

By definition, the "best lambda" value for a given alpha is the one that maximizes the quality function (QF) over the range of lambda values considered. Therefore, QF vs lambda distributions with sharp, narrow, well-defined peaks provide more confidence in the selection of the optimal lambda value than those with less-defined peaks. Sometimes, and particularly for the ridge (alpha=0) solutions, QF is observed to increase or decrease monotonically with lambda over its entire range, causing a boundary lambda value to be selected; we conservatively recommend to disregard alpha-models generated under such circumstances. If interested in investigating further, we suggest to re-run those alpha-models by extending the default range of lambda values (via the argument nlambda.ext) or its density (via the argument nlambda). On occasion, the range of lambda values is effectively limited by convergence issues of the underlying glmnet model; in such scenario, we recommend to increment the maximum allowed number of iterations (via the argument mxit, which is passed on to glmnet.control) or to limit the complexity of the model (e.g. by filtering and reducing the number of features fed into eNetXplorer).

See Also

eNetXplorer, plot

Examples

# NOT RUN {
data(QuickStartEx)
fit = eNetXplorer(x=QuickStartEx$predictor,y=QuickStartEx$response,
family="gaussian",n_run=20,n_perm_null=10,seed=111)
plot(x=fit,plot.type="lambdaVsQF")
plotLambdaVsQF(x=fit,alpha.index=c(1,3),main="custom title",col.main="red")
# }