ppc.find.splits {ppc}R Documentation

Function to find best discriminating split points for training data in mass spec

Description

Find best discriminating split points for training data (to separate the classes). Takes centroids.fit- result of call to make.centroids.list and peaks.fit- result of call to predict.peaks

Usage

ppc.find.splits(centroid.fit, peak.fit, data, user.parms)

Arguments

centroid.fit Result of call to ppc.make.centroid.list
peak.fit Result of call to ppc.predict.peaks
data List containing mass spec data
user.parms List of user defined parameters

Value

prhat Proportion of samples beyond optimal cutpoint in each outcome class
pr Proportion of samples beyond cutpoints in each outcome class
n.class number of samples in each outcome class
cutpoints Cutpoints (split points) tried
cuthat Optimal cut points
prclose Indicators for split points with prob difference within 10 percent of that of the optimal split point
nsplits Number of cutpoints tried
fix.at.one Was the optimal cutpoint fixed at one? (i.e no peak vs peak)

Author(s)

Balasubramanian Narasimhan and Rob Tibshirani

Examples

## for a complete worked example of this function in a PPC analysis  see
##  http://www-stat.stanford.edu/~tibs/PPC/Rdist/Rscript.rawdata

[Package ppc version 1.01 Index]