Description Usage Arguments Author(s) See Also Examples
View source: R/plot.eNetXplorer.R
This function is a wrapper for a variety of plots, namely:
summary
:
model performance across alpha
(to assess the relative performance among
different member models in the elastic net family, as well as in relation to permutation null models);
lambdaVsQF
:
given alpha
, quality function across lambda
(to examine the selection of the optimal penalty parameter);
measuredVsOOB
:
(for gaussian and categorical models) given alpha
, response vs outofbag predictions across instances (to assess
individual instances, examine outliers, etc);
contingency
:
(for categorical models) given alpha
, response vs outofbag predictions across classes;
featureCaterpillar
:
given alpha
, caterpillar plot of feature statistics compared to permutation
null models (with statistical significance annotations for individual features);
featureHeatmap
:
heatmap of feature statistics across alpha
(including statistical significance
annotations for individual features);
KaplanMeier
:
(for Cox regression models) given alpha
, KaplanMeier plot of survival probability as a function of time (where the cohort is partitioned in two or more groups based on predicted risk); and
survROC
:
(for Cox regression models) given alpha
, timedependent ROC plot(s) based on predicted risk at the specified timepoints of interest.
1 2 3 4 
x 

plot.type 
Type of plot to be produced. Available plots
are 
alpha.index 
Integer indices to select 
stat 
Feature statistic: 
... 
Additional plotting parameters. 
Julian Candia and John S. Tsang
Maintainer: Julian Candia julian.candia@nih.gov
eNetXplorer
, plotSummary
, plotLambdaVsQF
, plotMeasuredVsOOB
, plotContingency
,
plotFeatureCaterpillar
, plotFeatureHeatmap
, plotKaplanMeier
, plotSurvROC
1 2 3 4 5 6 7 8  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="summary")
plot(x=fit,plot.type="lambdaVsQF",alpha.index=2)
plot(x=fit,plot.type="measuredVsOOB",alpha.index=c(1,3,5))
plot(x=fit,plot.type="featureCaterpillar",stat="coef")
plot(x=fit,plot.type="featureHeatmap",stat="freq")

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