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Light version of PLS_lm for cross validation purposes

Source: R/PLS_lm_wvc.R
PLS_lm_wvc.Rd

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 ?

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

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.

Note

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

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://www.numdam.org/item/JSFS_2010__151_2_1_0/

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

Author

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

Examples


data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_lm_wvc(dataY=yCornell,dataX=XCornell,nt=3,dataPredictY=XCornell[1,])
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#> 
#> $valsPredict
#>       [,1]     [,2]     [,3]
#> 1 95.03164 96.49529 97.55864
#> 
PLS_lm_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),],
verbose=FALSE)
#> $valsPredict
#>       [,1]     [,2]     [,3]
#> 1 93.21260 93.91414 95.73889
#> 2 94.71773 95.68684 96.79566
#> 
PLS_lm_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),],
keepcoeffs=TRUE, verbose=FALSE)
#> $valsPredict
#>       [,1]     [,2]     [,3]
#> 1 93.21260 93.91414 95.73889
#> 2 94.71773 95.68684 96.79566
#> 
#> $coeffs
#>                     [,1]
#> tempConstante  90.036430
#> X1             -8.146409
#> X2             -4.000226
#> X3            -13.728783
#> X4             -5.883018
#> X5              6.937067
#> X6             10.148166
#> X7            -29.571048
#> 
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)
#> $valsPredict
#>           [,1]        [,2]          [,3]
#> 2   0.94076404  1.05174832  1.0374616113
#> 3   1.39047885  1.13453980  1.3242356673
#> 4   0.79354603  0.61363359  0.9634719738
#> 5   0.97860870  0.65429731  0.5150093803
#> 6   1.27787513  1.34466033  1.2141683374
#> 7   0.38074833 -0.19960869 -0.0297875729
#> 8   0.63365198  0.54155986  0.2091351671
#> 9   1.43295859  1.55738575  1.6867033620
#> 10  0.99298825  0.98045144  1.0732360517
#> 11  0.39646989  0.77799528  0.8878866176
#> 12 -0.06668771  0.22196126  0.6489885307
#> 13  1.66741434  1.86115065  2.0625005103
#> 14  1.30880185  1.68986944  1.5677761570
#> 15  1.23730000  1.38593444  1.3409666162
#> 16 -0.25157291 -0.43528542 -0.4743325478
#> 17  0.11040475  0.12039192  0.0007238673
#> 18  0.92497736  0.93090871  0.9139450905
#> 19  1.07369519  0.75362958  0.8101882969
#> 20  0.28649448  0.01372797 -0.0381486634
#> 21  0.07611695  0.25760359  0.4947474762
#> 22  0.62440377  0.61179280  0.7213780672
#> 23  0.90767675  1.00727900  0.7150222566
#> 24  0.32767392  0.33311540  0.3319260445
#> 25  0.52077680  0.11553149 -0.1540043757
#> 26  0.90533669  0.96712444  0.7408564424
#> 27  0.03199415  0.35664666  0.1497469324
#> 28  1.47696322  1.10783727  1.0880798820
#> 29  1.39868360  1.45100576  1.5321709218
#> 30  0.71071212  0.65722183  0.8296817343
#> 31  0.95366454  1.27340221  1.1747095867
#> 32  0.67802770  1.02264538  0.9118337341
#> 33  0.27905267  0.23984263  0.1497228439
#> 
PLS_lm_wvc(dataY=ypine[-1],dataX=XpineNAX21[-1,],nt=3,dataPredictY=XpineNAX21[1,], verbose=FALSE)
#> list()
PLS_lm_wvc(dataY=ypine[-2],dataX=XpineNAX21[-2,],nt=3,dataPredictY=XpineNAX21[2,], verbose=FALSE)
#> $valsPredict
#>        [,1]     [,2]     [,3]
#> 2 0.9914237 1.127694 1.076871
#> 
PLS_lm_wvc(dataY=ypine,dataX=XpineNAX21,nt=3, verbose=FALSE)
#> $valsPredict
#>                [,1]        [,2]        [,3]
#>  [1,]  1.4589428372  2.11593441  2.26945463
#>  [2,]  0.9829728848  1.18076301  1.03620773
#>  [3,]  1.5465793485  1.18650144  1.27588027
#>  [4,]  0.8655200475  0.46560150  0.87695905
#>  [5,]  1.1072893206  0.85251283  0.57894070
#>  [6,]  1.3475354913  1.50304781  1.20343678
#>  [7,]  0.4642031730 -0.42135911 -0.09955758
#>  [8,]  0.7227037635  0.98797643  0.21698630
#>  [9,]  1.4817373396  1.44514851  1.73358055
#> [10,]  1.0254800667  0.83934144  1.03617380
#> [11,]  0.3387272475  0.80689483  1.16252267
#> [12,] -0.1404993319  0.07995556  0.28848700
#> [13,]  1.7330197324  1.78515568  2.09996156
#> [14,]  1.3042461768  1.77459300  1.45332343
#> [15,]  1.2715747058  1.40954157  1.32075175
#> [16,] -0.2777168878 -0.44870259 -0.51434295
#> [17,]  0.0732828651  0.16650220 -0.07993936
#> [18,]  1.0136353777  1.18186572  1.21367764
#> [19,]  1.1731943384  0.65036199  0.69946245
#> [20,]  0.3361806444  0.07862672 -0.02520022
#> [21,]  0.0005399781  0.08838604  0.42008495
#> [22,]  0.6493952920  0.59285401  0.78398392
#> [23,]  0.9096512309  1.09049865  0.76180402
#> [24,]  0.2922013348  0.21706236  0.34621654
#> [25,]  0.6382767422  0.42340484 -0.21649161
#> [26,]  0.9211966658  1.04536567  0.90202594
#> [27,] -0.0650662222  0.48221488  0.31920563
#> [28,]  1.6349387353  1.13681799  1.30914490
#> [29,]  1.4578617140  1.36487443  1.62299471
#> [30,]  0.7469063021  0.56602059  1.06652525
#> [31,]  0.9273242679  1.29767879  1.02671404
#> [32,]  0.6059700763  0.95665914  1.21783413
#> [33,]  0.2450768211  0.13732755  0.12831479
#> 
rm("ypine")