PLR: A function which performs penalised logistic regression... In MCRestimate: Misclassification error estimation with cross-validation

Description

A function which performs penalised logistic regression.

Usage

 ```1 2 3``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```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") } ```

MCRestimate documentation built on May 2, 2018, 2:05 a.m.