Extracts coefficients from k-fold cross validated partial least squares regression models

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

kfolds2coeff(pls_kfolds)

Arguments

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

Value

coef.all: matrix with the values of the coefficients for each leave one out step or NULL if another type of cross validation was used.

Details

This fonctions works for plsR and plsRglm models.

Note

Only for NK=1 and leave one out CV

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

Author

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

Examples


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)
#>           [,1]       [,2]      [,3]      [,4]      [,5]        [,6]      [,7]
#>  [1,] 90.87298  -7.730285 -5.317939 -13.05518 -7.612449  10.6111677  9.346481
#>  [2,] 92.08375 -10.476510 -5.782367 -17.77961 -7.207449  -7.0528029 10.556420
#>  [3,] 91.99726  -9.787064 -6.457435 -16.59124 -7.879221  -5.1780580 11.296555
#>  [4,] 93.11223  -9.180395 -7.969940 -15.39982 -9.450540  -0.4784392  9.484642
#>  [5,] 93.60436 -10.510020 -7.605163 -17.88485 -9.971635 -11.5650708  9.778748
#>  [6,] 94.86937 -11.914366 -7.224898 -20.18752 -8.149944  -8.9696175  8.585089
#>  [7,] 92.23105  -9.632494 -6.603052 -16.34906 -8.168203  -3.6682514 10.603008
#>  [8,] 92.46569  -9.885233 -6.735075 -16.81921 -8.272827  -4.0839573 10.373591
#>  [9,] 92.46387  -9.579230 -6.777947 -16.25454 -8.298890  -4.1484044 10.358129
#> [10,] 92.60529  -9.691082 -6.914517 -16.46361 -8.382754  -4.2807356 10.236205
#> [11,] 92.36316  -8.747489 -6.589638 -14.87073 -8.309297  -3.4465827 10.445493
#> [12,] 93.42592 -11.114193 -8.038234 -18.87582 -8.783616  -6.9236077  9.690647
#>            [,8]
#>  [1,] -29.60954
#>  [2,] -33.45639
#>  [3,] -31.02182
#>  [4,] -34.89010
#>  [5,] -34.37588
#>  [6,] -53.16098
#>  [7,] -33.39444
#>  [8,] -34.49712
#>  [9,] -33.70987
#> [10,] -34.34320
#> [11,] -34.82607
#> [12,] -31.84496
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)
#>           [,1]         [,2]        [,3]       [,4]       [,5]       [,6]
#>  [1,] 7.814246 -0.002869674 -0.03549716 0.02107684 -0.2219176 0.07293649
#>  [2,] 8.361383 -0.002854043 -0.03743841 0.02249864 -0.2164871 0.06909414
#>  [3,] 8.650702 -0.002814409 -0.03797563 0.02335471 -0.2733051 0.07768935
#>  [4,] 8.558683 -0.002769261 -0.03941925 0.02261582 -0.2486856 0.07253035
#>  [5,] 8.218008 -0.002808356 -0.03512320 0.02344851 -0.2380859 0.07483498
#>  [6,] 8.305299 -0.002728968 -0.03775406 0.02226888 -0.2278873 0.07310943
#>  [7,] 8.544910 -0.002866033 -0.04077690 0.02255038 -0.1986620 0.08297200
#>  [8,] 8.071791 -0.002768139 -0.03735800 0.01911647 -0.1574095 0.06347242
#>  [9,] 7.713087 -0.002301366 -0.03350332 0.02179469 -0.1982322 0.06886116
#> [10,] 8.193386 -0.002427074 -0.03899699 0.02178240 -0.2476962 0.07823805
#> [11,] 8.878504 -0.003085315 -0.03992126 0.01865159 -0.2000119 0.06161356
#> [12,] 9.117046 -0.003179412 -0.03520851 0.02509672 -0.2355585 0.07948714
#> [13,] 7.765163 -0.002551739 -0.03589722 0.02021236 -0.2297687 0.07507578
#> [14,] 8.298677 -0.002659646 -0.03597977 0.02186039 -0.2382632 0.06852447
#> [15,] 8.306726 -0.002731392 -0.03769351 0.02271611 -0.2067318 0.06867246
#> [16,] 8.700332 -0.003000855 -0.03968194 0.02031214 -0.2439055 0.07971910
#> [17,] 8.363382 -0.002725646 -0.03760331 0.02239444 -0.2284354 0.07265824
#> [18,] 8.297162 -0.002703300 -0.03809335 0.02228154 -0.2452161 0.07683082
#> [19,] 8.262983 -0.002749134 -0.03604436 0.02181592 -0.2560591 0.07712466
#> [20,] 8.876407 -0.003129747 -0.04096829 0.02442558 -0.2257238 0.07799168
#> [21,] 8.201937 -0.002817385 -0.04037497 0.01866908 -0.2256173 0.07206968
#> [22,] 8.284944 -0.002533698 -0.04058031 0.02165310 -0.2387052 0.07384772
#> [23,] 8.797446 -0.002860185 -0.03849982 0.02287225 -0.2398173 0.06896626
#> [24,] 7.929182 -0.002433262 -0.04104596 0.02373045 -0.2080378 0.07196279
#> [25,] 7.989959 -0.002757493 -0.04265244 0.02273451 -0.2204082 0.07554612
#> [26,] 8.387502 -0.002676524 -0.03883171 0.02166663 -0.2438722 0.07389119
#> [27,] 8.472460 -0.002825763 -0.03726580 0.02081686 -0.2401187 0.07506551
#> [28,] 7.911123 -0.002181740 -0.03810400 0.01767450 -0.1808041 0.04804859
#> [29,] 8.351547 -0.002759590 -0.03948543 0.02215742 -0.2302882 0.07157444
#> [30,] 8.433206 -0.002887146 -0.03839730 0.02024058 -0.2138490 0.06996299
#> [31,] 8.443818 -0.003194742 -0.03679137 0.02566206 -0.2401347 0.09821587
#> [32,] 8.304524 -0.003016708 -0.03992431 0.02217589 -0.1715886 0.06046621
#> [33,] 8.457649 -0.002769321 -0.03834922 0.02197341 -0.2326822 0.07359720
#>            [,7]       [,8]        [,9]      [,10]      [,11]
#>  [1,] 0.2447542 -0.2023747 -0.08867027 -0.6756387 -0.3860024
#>  [2,] 0.2508868 -0.4458012 -0.08263970 -0.7356824 -0.3964378
#>  [3,] 0.2606680 -0.4565363 -0.09217752 -0.7261476 -0.4746015
#>  [4,] 0.2586394 -0.5379771 -0.08575226 -0.7061042 -0.4206479
#>  [5,] 0.2133057 -0.3908510 -0.08249627 -0.7398298 -0.3869324
#>  [6,] 0.2557966 -0.5581856 -0.08777483 -0.7309410 -0.3373923
#>  [7,] 0.2140205 -0.5802810 -0.10056348 -0.7089801 -0.3989933
#>  [8,] 0.2412951 -0.5263135 -0.09178826 -0.7010010 -0.2785220
#>  [9,] 0.2802143 -0.5919984 -0.07268121 -0.8076147 -0.4227010
#> [10,] 0.2415159 -0.6658565 -0.08619423 -0.7333098 -0.3641896
#> [11,] 0.2358135 -0.5397474 -0.06518898 -0.6304151 -0.5049983
#> [12,] 0.2670978 -0.9590753 -0.08518518 -0.6965007 -0.2282369
#> [13,] 0.2021548 -0.4795504 -0.07983647 -0.6572603 -0.3362084
#> [14,] 0.2422333 -0.6637845 -0.07746177 -0.7198954 -0.2925045
#> [15,] 0.2466642 -0.5335098 -0.09370588 -0.6981061 -0.3848060
#> [16,] 0.2507118 -0.4494593 -0.09915981 -0.6924289 -0.4327700
#> [17,] 0.2490739 -0.5898428 -0.08451685 -0.7448365 -0.3305126
#> [18,] 0.2564153 -0.5118346 -0.08873345 -0.7275463 -0.3785027
#> [19,] 0.2611840 -0.4432492 -0.09082157 -0.7323138 -0.3756275
#> [20,] 0.2454048 -0.4253930 -0.09794089 -0.7144557 -0.4743556
#> [21,] 0.2600754 -0.3528332 -0.07860586 -0.6946200 -0.4181529
#> [22,] 0.2715177 -0.6308057 -0.09274944 -0.7124415 -0.3325791
#> [23,] 0.2610561 -0.6266037 -0.08359975 -0.6979821 -0.4452225
#> [24,] 0.2350859 -0.5378953 -0.09799516 -0.7206555 -0.2957781
#> [25,] 0.2374591 -0.3632903 -0.08893607 -0.7293388 -0.2622970
#> [26,] 0.2396266 -0.5755539 -0.08208888 -0.7096107 -0.3818814
#> [27,] 0.2637412 -0.4868311 -0.08269255 -0.7191352 -0.4532000
#> [28,] 0.2315868 -0.6747620 -0.08792043 -0.7548252 -0.2050949
#> [29,] 0.2622808 -0.5122030 -0.08093838 -0.7864637 -0.2986094
#> [30,] 0.2040295 -0.5588206 -0.10558610 -0.5334666 -0.3581429
#> [31,] 0.2718858 -0.3416514 -0.10653476 -0.8057978 -0.3245760
#> [32,] 0.1956558 -0.3607404 -0.06637462 -0.7149729 -0.3418038
#> [33,] 0.2524494 -0.5331250 -0.08739496 -0.7228717 -0.4052381
boxplot(kfolds2coeff(bbb2)[,1])

rm(list=c("Xpine","ypine","bbb2"))