PLS_lm_wvc: Light version of PLS_lm for cross validation purposes

In fbertran/plsRglm: Partial Least Squares Regression for Generalized Linear Models

Light version of PLS_lm for cross validation purposes

Description

Light version of PLS_lm for cross validation purposes either on complete or incomplete datasets.

Usage

PLS_lm_wvc(
  dataY,
  dataX,
  nt = 2,
  dataPredictY = dataX,
  modele = "pls",
  scaleX = TRUE,
  scaleY = NULL,
  keepcoeffs = FALSE,
  keepstd.coeffs = FALSE,
  tol_Xi = 10^(-12),
  weights,
  verbose = TRUE
)

Arguments

dataY	response (training) dataset
dataX	predictor(s) (training) dataset
nt	number of components to be extracted
dataPredictY	predictor(s) (testing) dataset
modele	name of the PLS model to be fitted, only ("pls" available for this fonction.
scaleX	scale the predictor(s) : must be set to TRUE for modele="pls" and should be for glms pls.
scaleY	scale the response : Yes/No. Ignored since non always possible for glm responses.
keepcoeffs	whether the coefficients of unstandardized eXplanatory variables should be returned or not.
keepstd.coeffs	whether the coefficients of standardized eXplanatory variables should be returned or not.
tol_Xi	minimal value for Norm2(Xi) and \mathrm{det}(pp' \times pp) if there is any missing value in the dataX. It defaults to 10^{-12}
weights	an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
verbose	should info messages be displayed ?

Details

This function is called by PLS_lm_kfoldcv in order to perform cross-validation either on complete or incomplete datasets.

Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.

Value

valsPredict	nrow(dataPredictY) * nt matrix of the predicted values
list("coeffs")	If the coefficients of the eXplanatory variables were requested: i.e. keepcoeffs=TRUE. ncol(dataX) * 1 matrix of the coefficients of the the eXplanatory variables

Note

Use PLS_lm_kfoldcv for a wrapper in view of cross-validation.

Author(s)

Frédéric Bertrand
[e-mail]
https://fbertran.github.io/homepage/

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. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/47

See Also

PLS_lm for more detailed results, PLS_lm_kfoldcv for cross-validating models and PLS_glm_wvc for the same function dedicated to plsRglm models

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_lm_wvc(dataY=yCornell,dataX=XCornell,nt=3,dataPredictY=XCornell[1,])
PLS_lm_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),],
verbose=FALSE)
PLS_lm_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),],
keepcoeffs=TRUE, verbose=FALSE)
rm("XCornell","yCornell")

## With an incomplete dataset (X[1,2] is NA)
data(pine)
ypine <- pine[,11]
data(XpineNAX21)
PLS_lm_wvc(dataY=ypine[-1],dataX=XpineNAX21[-1,],nt=3, verbose=FALSE)
PLS_lm_wvc(dataY=ypine[-1],dataX=XpineNAX21[-1,],nt=3,dataPredictY=XpineNAX21[1,], verbose=FALSE)
PLS_lm_wvc(dataY=ypine[-2],dataX=XpineNAX21[-2,],nt=3,dataPredictY=XpineNAX21[2,], verbose=FALSE)
PLS_lm_wvc(dataY=ypine,dataX=XpineNAX21,nt=3, verbose=FALSE)
rm("ypine")

fbertran/plsRglm documentation built on March 23, 2023, 2:14 a.m.