Computes PRESS for k-fold cross validated partial least squares regression models.

This function computes PRESS for k-fold cross validated partial least squares regression models.

Usage

kfolds2Press(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression model

Value

list

Press vs number of components for the first group partition

list()

...

list

Press vs number of components for the last group partition

Note

Use cv.plsR to create k-fold cross validated partial least squares regression models.

References

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. https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/47/

See also

kfolds2coeff, kfolds2Pressind, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross validation.

Author

Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
kfolds2Press(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=12,NK=1,verbose=FALSE))
#> [[1]]
#> [1] 55.70774 41.43274 20.27397 21.24240 24.51801
#> 
kfolds2Press(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=6,NK=1,verbose=FALSE))
#> [[1]]
#> [1] 55.56262 40.00977 19.05723 19.30291 22.60499
#> 
rm(list=c("XCornell","yCornell"))

# \donttest{
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
kfolds2Press(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=1,verbose=FALSE))
#> [[1]]
#>  [1] 12.17362 13.32722 11.89863 10.76002 10.78439 10.87965 11.09345 13.43135
#>  [9] 14.32411 14.54937
#> 
kfolds2Press(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=2,verbose=FALSE))
#> [[1]]
#>  [1] 12.728449 11.554432  9.794321  9.631880  9.640802  9.904351 10.462499
#>  [8] 10.344699 10.385719 10.604705
#> 
#> [[2]]
#>  [1] 13.82029 15.47818 15.22997 13.84117 14.29024 14.38570 15.81367 16.75103
#>  [9] 17.70783 18.81556
#> 

XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
kfolds2Press(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=1,verbose=FALSE))
#> [[1]]
#> [1] 13.94508 14.72641 14.90373 15.47681 15.55398 15.75101 16.21811 18.29196
#> [9] 19.58054
#> 
kfolds2Press(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=2,verbose=FALSE))
#> [[1]]
#> [1] 14.44350 14.29197 15.49908 16.12835 24.10455 18.42922 19.68966 25.39844
#> [9] 24.77465
#> 
#> [[2]]
#> [1] 15.55066 17.75194 16.72691 14.86151 15.45137 15.88554 16.09298 22.61966
#> [9] 22.42832
#> 
rm(list=c("Xpine","XpineNAX21","ypine"))
# }