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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