PLR {MCRestimate}R Documentation

A function which performs penalised logistic regression classification for two groups

Description

A function which performs penalised logistic regression.

Usage

PLR(trainmatrix, resultvector, kappa=0, eps=1e-4)
       predict.PLR(object,...)

Arguments

resultvector a vector which contains the labeling of the samples
trainmatrix a matrix which includes the data. The rows corresponds to the observations and the colums to the variables.
kappa value range for penalty parameter. If more that one parameter is specified the one with the lowest AIC will be used.
eps
object a fitted PLR model
... here a data matrix from samples that should be predicted

Details

Value

a list with three arguments

a Intercept estimate of the linear predictor
b vector of estimated regresion coefficients
factorlevel levels of grouping variable
aics vector of AIC values with respect to penalty parameter kappa
trs vector of effective degrees of freedom with respect to penalty parameter kappa

Author(s)

Axel Benner, Ulrich Mansmann, based on MathLab code by Paul Eilers

Examples

library(golubEsets)
data(golubMerge)
eSet<-golubMerge
X0 <- t(exprs(eSet))
m <- nrow(X0); n <- ncol(X0)
y <- pData(eSet)$ALL.AML
f <- PLR(X0, y,kappa=10^seq(0, 7, 0.5))
if (interactive()) {
  x11(width=9, height=4)
  par(mfrow=c(1,2))
  }
plot(log10(f$kappas), f$aics, type="l",main="Akaike's Information Criterion", xlab="log kappa", ylab="AIC")
plot(log10(f$kappas), f$trs, type="l",xlab="log kappa", ylab="Dim",main="Effective dimension")

[Package MCRestimate version 1.4.0 Index]