plotLambdaVsQF | R Documentation |
lambda
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.
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", ...)
x |
|
alpha.index |
Integer indices to select |
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. |
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
).
Julian Candia and John S. Tsang
Maintainer: Julian Candia julian.candia@nih.gov
eNetXplorer
, plot
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")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.