Description Usage Arguments Details Value Author(s) References See Also Examples
This function builds a prediction rule based on the learning data (microarray predictors only) and applies it to the test data. The classifier consists of two steps: PLS dimension reduction (without pre-validation step) for summarizing microarray data, and random forests applied to the obtained PLS components. See Boulesteix et al (2008) for more details.
The function plsrf_x
uses the functions cforest
and varimp
from the package party
and the function
pls.regression
from the package plsgenomics
.
1 |
Xlearn |
A nlearn x p matrix giving the microarray predictors for the learning data set. |
Zlearn |
A nlearn x q matrix giving the clinical predictors for the learning data set. This argument is ignored. |
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. |
Ztest |
A ntest x q matrix giving the clinical predictors for the test data set. This argument is ignored. |
ncomp |
A numeric vector giving the candidate numbers of PLS components. All numbers must be >0. |
ordered |
A vector of length p giving the order of the microarray predictors in terms of relevance for prediction. For instance, if the three first elements of |
nbgene |
The number of genes to be selected for use in dimension reduction. Default is |
... |
Other arguments to be passed to the function |
See Boulesteix et al (2008).
A list with the elements:
prediction |
A numeric vector of length |
importance |
The variable importance information output
by the function |
bestncomp |
The best number of PLS components, as obtained using the model selection method based on the out-of-bag error. |
OOB |
A numeric vector of length |
Anne-Laure Boulesteix (http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/eng.html)
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
, plsrf_xz_pv
, rf_z
,
logistic_z
, svm_x
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # load MAclinical library
# library(MAclinical)
# Generating xlearn, zlearn, ylearn, xtest, ztest
xlearn<-matrix(rnorm(3000),30,100)
zlearn<-matrix(rnorm(120),30,4)
ylearn<-sample(0:1,30,replace=TRUE)
xtest<-matrix(rnorm(2000),20,100)
ztest<-matrix(rnorm(80),20,4)
my.prediction1<-plsrf_x(Xlearn=xlearn,Ylearn=ylearn,Xtest=xtest)
ordered<-sample(100)
my.prediction2<-plsrf_x(Xlearn=xlearn,Ylearn=ylearn,Xtest=xtest,ordered=ordered,nbgene=20)
|
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