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

Finds the optimal number of component for LS-PCR model for logistic regression.

1 | ```
cv.lspcr.glm(Y, X, D, ncompmax, folds = 5, proportion = 0.9)
``` |

`Y` |
a vector of length |

`X` |
a data matrix ( |

`D` |
a data matrix ( |

`ncompmax` |
a positive integer. |

`folds` |
a positive integer indicating the number of folds in K-folds cross-validation procedure. |

`proportion` |
proportion of the dataset in the learning sample. |

This function finds the optimal number of component for a LS-PCR model. At each cross validation run, `X`

, `D`

and `Y`

are split into one training set
and one test set (of proportion `proportion`

and `1-proportion`

). Then the classification error rate is computed for each value of `ncomp`

between 1 and `ncompmax`

. At the end we choose the number of component for which the classification error rate is minimal. This function returns also `p.cvg`

. It's a vector of size `ncompmax`

which contains convergence proportion of the logistic regression for each number of component between 1 and `ncompmax`

.

`ncompopt ` |
the optimal number of component. |

`p.cvg ` |
convergence proportion of the logistic regression. |

Caroline Bazzoli, Thomas Bouleau, Sophie Lambert-Lacroix

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
#data
data(BreastCancer)
#vector of responses
Y<-BreastCancer$Y
#Genetic data
X<-BreastCancer$X
#Clinical data
D<-BreastCancer$D
#SIS selection
X<-scale(X)
X<-SIS.selection(X=X,Y=Y,pred=50)
#cross validation to find the optimal number of component
cv<-cv.lspcr.glm(Y=Y,X=X,D=D,folds=5,ncompmax=5,proportion=0.9)
ncompopt<-cv$ncompopt
``` |

lsplsGlm documentation built on July 27, 2017, 5:01 p.m.

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