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

Fits a model the combination of two methods: Ordinary Least Square (OLS) and Principal Component Regression (PCR) to fit both clinical and gene expression data.

1 | ```
fit.lspcr.glm(Y,X,D,ncomp)
``` |

`Y` |
a vector of length |

`X` |
a data matrix ( |

`D` |
a data matrix ( |

`ncomp` |
a positive integer. |

This function combines two methods, the first one is the Principal Components Regression on genetic data to reduce the dimension using `prcomp`

from {`stats`

} package. The second one is the logistic regression on the concatenation of the `ncomp`

first selected axes and clinical data (`D`

) to explain `Y`

. To do that we use `glm`

from {`stats`

} package.

`coefficients ` |
coefficients of logistic regression. |

`cvg ` |
the 0-1 value indicating convergence of the algorithm (1 for convergence, 0 otherwise). |

`projection ` |
projection matrix used to convert |

Caroline Bazzoli, Thomas Bouleau, Sophie Lambert-Lacroix

`cv.lspcr.glm`

,`predict.lspcr.glm`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
#Data
data(BreastCancer)
#Vector of response
Y<-BreastCancer$Y
#Genetic data
X<-BreastCancer$X
#Clinical data
D<-BreastCancer$D
#Apply fit.lspcr.glm with ncomp=5 using the 76 first patients
fit<-fit.lspcr.glm(Y=Y[1:76],X=X[1:76,],D=D[1:76,],ncomp=5)
#using projection to predict class of 2 last patients
newX<-X[77:78,]
newD<-D[77:78,]
#New Score matrix
newScores<-newX%*%fit$projection
#prediction
newEta=cbind(rep(1,dim(newD)[1]),newD,newScores)%*%fit$coefficients
newPi=1/(1+exp(-newEta))
newY=as.numeric(newEta>0)
``` |

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