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]