PLR {MCRestimate} | R Documentation |
A function which performs penalised logistic regression.
PLR(trainmatrix, resultvector, kappa=0, eps=1e-4) predict.PLR(object,...)
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 |
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 |
Axel Benner, Ulrich Mansmann, based on MathLab code by Paul Eilers
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")