plot.mccv: Plotting single or multiple ROC curves of the cross-validated...

In pjpalla/RFmarkerDetector: Multivariate Analysis of Metabolomics Data using Random Forests

Description

Plotting single or multiple ROC curves of the cross-validated Random Forest models plot.mccv allows to plot single or multiple ROC curves to characterize the performace of a cross-validated Random Forest model

Usage

## S3 method for class 'mccv'
plot(x, y, ..., opt = list(avg = "vertical", colorize = F))

Arguments

x	an object of class mccv
y	not used
...	optional graphical parameters
opt	a list containing the following optional parameters: avg if the mccv object represents the predictions obtained from different cross-validation runs, we can have a different roc curve for each cv run. These curves can be averaged or not. Allowed values are none (plot all curves separately), horizontal (horizontal averaging), vertical(vertical averaging) and threshold (threshold averaging). colorize a logical value which indicates if the curve(s) shoud be colorized according to the cutoff.

Author(s)

Piergiorgio Palla

Examples

## data(cachexiaData)
## params <- list(ntrees = 500, ref_level = levels(cachexiaData[,2])[1] )
## mccv_obj <- rfMCCV(cachexiaData, nsplits = 50, test_prop = 1/3, opt_params = params)
## params = list(avg = 'vertical', colorize = FALSE) 
## plot.mccv(mccv_obj, opt = params)

pjpalla/RFmarkerDetector documentation built on May 25, 2019, 8:19 a.m.