A function which performs penalised logistic regression classification for two groups

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Description

A function which performs penalised logistic regression.

Usage

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PLR(trainmatrix, resultvector, kappa=0, eps=1e-4)
       ## S3 method for class 'PLR'
predict(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 columns 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

precision of convergence

object

a fitted PLR model

...

here a data matrix from samples that should be predicted

Value

a list with three arguments

a

Intercept estimate of the linear predictor

b

vector of estimated regression 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

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library(golubEsets)
data(Golub_Merge)
eSet<-Golub_Merge
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")
}