kfolds2coeff | R Documentation |

This fonction extracts coefficients from k-fold cross validated partial least squares regression models

```
kfolds2coeff(pls_kfolds)
```

`pls_kfolds` |
an object that is a k-fold cross validated partial least squares regression models either lm or glm |

This fonctions works for plsR and plsRglm models.

`coef.all` |
matrix with the values of the coefficients for each
leave one out step or |

Only for `NK=1`

and leave one out CV

Frédéric Bertrand

frederic.bertrand@utt.fr

https://fbertran.github.io/homepage/

Nicolas Meyer, Myriam Maumy-Bertrand et
Frédéric Bertrand (2010). Comparing the linear and the
logistic PLS regression with qualitative predictors: application to
allelotyping data. *Journal de la Societe Francaise de Statistique*,
151(2), pages 1-18.
http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/47

`kfolds2Pressind`

, `kfolds2Press`

,
`kfolds2Mclassedind`

, `kfolds2Mclassed`

and
`summary`

to extract and transform
results from k-fold cross validation.

```
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
bbb <- PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=nrow(XCornell),keepcoeffs=TRUE,
verbose=FALSE)
kfolds2coeff(bbb)
boxplot(kfolds2coeff(bbb)[,2])
rm(list=c("XCornell","yCornell","bbb"))
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb2 <- cv.plsR(object=ypine,dataX=Xpine,nt=4,K=nrow(Xpine),keepcoeffs=TRUE,verbose=FALSE)
kfolds2coeff(bbb2)
boxplot(kfolds2coeff(bbb2)[,1])
rm(list=c("Xpine","ypine","bbb2"))
```

plsRglm documentation built on March 31, 2023, 11:10 p.m.

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