Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/testclass_simul.r

This function evaluates classifiers built using microarray data and/or clinical predictors, based on
simulated data generated using the functions `simuldata_list`

and `simuldatacluster_list`

(see `simulate`

).

1 2 | ```
testclass_simul(datalist,nlearn=100,classifier,ncomp=0:3,nbgene=NULL,
varsel=NULL,fold=10,...)
``` |

`datalist` |
A list of niter simulated data sets as generated by the functions |

`nlearn` |
The number of observations to be included in the learning data set. It must be smaller than the total number of observations of the data sets. |

`classifier` |
The function used to construct a classifier. The function must have the same structure as |

`ncomp` |
The candidate numbers of PLS components (if PLS dimension reduction is used). |

`nbgene` |
The number of genes to use for classifier construction. Default is |

`varsel` |
A niter x p matrix giving the indices of the genes ordered by the chosen gene selection criterion. For example, the element in the first row and the first column is the index of the gene that is ranked best using in the first simulation iteration. |

`fold` |
The number of folds for the pre-validation step, if any. See Boulesteix et al (2008) for more details. Default is |

`...` |
Other arguments to be passed to the function |

See Boulesteix et al (2008).

`error` |
A numeric vector of length |

`bestncomp` |
A numeric vector of length |

`OOB` |
A list 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`

, `plsrf_xz_pv`

, `simulate`

,
`plsrf_xz_pv`

, `plsrf_x_pv`

, `plsrf_xz`

, `plsrf_x`

,
`rf_z`

, `svm_x`

, `logistic_z`

.

1 2 3 4 5 6 7 8 9 10 | ```
# load MAclinical library
# library(MAclinical)
# Generating 3 simulated data sets
my.data<-simuldata_list(niter=3,n=100,p=150,psig=10,q=5,muX=2,muZ=1)
# Perform prediction of the 60 last observations using the first 40 observations,
# based on PLS (without pre-validation) and random forests
testclass_simul(my.data,nlearn=40,classifier=plsrf_xz)
``` |

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