logistic_z: Class prediction based on logistic regression using clinical...

In MAclinical: Class prediction based on microarray data and clinical parameters

Description

This function builds a prediction rule based on the learning data (clinical predictors only) and applies it to the test data. It uses the function glm.

Usage

logistic_z(Xlearn=NULL,Zlearn,Ylearn,Xtest=NULL,Ztest,...)

Arguments

Xlearn	A nlearn x p matrix giving the microarray predictors for the learning data set. This argument is ignored.
Zlearn	A nlearn x q matrix giving the clinical predictors for the learning data set.
Ylearn	A numeric vector of length nlearn giving the class membership of the learning observations, coded as 0,1.
Xtest	A ntest x p matrix giving the microarray predictors for the test data set. This argument is ignored.
Ztest	A ntest x q matrix giving the clinical predictors for the test data set.
...	Other arguments.

Details

See Boulesteix et al (2008).

Value

A list with the element:

prediction

A numeric vector of length nrow(Xtest) giving the predicted class for each observation from the test data set.

Author(s)

Anne-Laure Boulesteix (http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/eng.html)

References

Boulesteix AL, Porzelius C, Daumer M, 2008. Microarray-based classification and clinical predictors: On combined classifiers and additional predictive value. Bioinformatics 24:1698-1706.

See Also

testclass, testclass_simul, simulate, plsrf_x_pv, plsrf_xz_pv, plsrf_x, plsrf_xz, rf_z, svm_x.

Examples

# load MAclinical library
# library(MAclinical)

# Generating zlearn, ylearn, ztest
zlearn<-matrix(rnorm(120),30,4)
ylearn<-sample(0:1,30,replace=TRUE)
ztest<-matrix(rnorm(80),20,4)

my.prediction<-logistic_z(Zlearn=zlearn,Ylearn=ylearn,Ztest=ztest)
my.prediction

MAclinical documentation built on May 2, 2019, 9:30 a.m.