rf_z {MAclinical} | R Documentation |
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 cforest
from the package party
. See Boulesteix et al (2008) for more details.
rf_z(Xlearn=NULL,Zlearn,Ylearn,Xtest=NULL,Ztest,...)
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,...,K-1 (where K is the number of classes). |
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 to be passed to the function cforest_control from the party package. |
See Boulesteix et al (2008).
A list with the elements:
prediction |
A numeric vector of length nrow(Xtest) giving the predicted class for
each observation from the test data set. |
importance |
The variable importance information output
by the function varimp from the package party . |
OOB |
The out-of-bag error of the constructed forest. |
Anne-Laure Boulesteix (http://www.slcmsr.net/boulesteix)
Boulesteix AL, Porzelius C, Daumer M, 2008. Microarray-based classification and clinical predictors: On combined classifiers and additional predictive value. Bioinformatics 24:1698-1706.
testclass
, testclass_simul
, simulate
,
plsrf_x_pv
, plsrf_xz_pv
, plsrf_x
, plsrf_xz
,
logistic_z
, svm_x
.
# 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<-rf_z(Zlearn=zlearn,Ylearn=ylearn,Ztest=ztest) my.prediction