get_roc: get_roc

In kuppal2/xmsPANDA: R Package for Biomarker Discovery, Network, and Data Exploratory Analysis

Description

This function generates Receiver Operating Characteristic curves using SVM and Logistic Regression as classifiers.

Usage

get_roc(dataA, classlabels, classifier = "svm", kname = "radial", 
rocfeatlist = NA, rocfeatincrement = TRUE, testset = NA, 
testclasslabels = NA, mainlabel = NA, col_lab = NA, 
legend = TRUE, newdevice = FALSE, mz_names = NA, 
svm.type = "nu-classification")

Arguments

dataA	R object for feature table with only differentially expressed features. This is the training set. The first two columns should be m/z and time.
classlabels	Class labels vector. e.g. c("case","control","case")
classifier	Classification algorithm to be used for ROC analysis. svm: Support Vector Machine logitreg: Logistic Regression eg: "svm" or "logitreg"
kname	Kernel for SVM. eg: "radial"
rocfeatlist	Vector indicating number of features to be used for ROC evaluation: eg: c(2,4,6) will generate ROC for top 2, top 4, and top 6 feautres. Default: seq(2,10,1)
rocfeatincrement	Turns on or off forward selection. eg: TRUE or FALSE
testset	R object for test feature table with only differentially expressed features. This is the test set. The first two columns should be m/z and time. The order of features should be same as the training set.
testclasslabels	Class labels vector for samples in the test set.
mainlabel	Main text label for the ROC plot. eg: "Group A vs B ROC curve"

Details

Function to perform ROC curve analysis using only traning set or using both training and test set.

Value

PDF file with ROC plot

Author(s)

Karan Uppal; [e-mail]

kuppal2/xmsPANDA documentation built on May 15, 2021, 5:48 a.m.