R/kfolds2CVinfos_glm.R
kfolds2CVinfos_glm.RdThis function extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm models for both formula or classic specifications of the model.
kfolds2CVinfos_glm(pls_kfolds, MClassed = FALSE, verbose = TRUE)
| pls_kfolds | an object computed using |
|---|---|
| MClassed | should number of miss classed be computed ? |
| verbose | should infos be displayed ? |
table of fit statistics for first group partition
...
table of fit statistics for last group partition
The Mclassed option should only set to TRUE if the response is
binary.
Use summary and cv.plsRglm instead.
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
kfolds2coeff, kfolds2Pressind,
kfolds2Press, kfolds2Mclassedind and
kfolds2Mclassed to extract and transforms results from k-fold
cross-validation.
Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
# \donttest{ data(Cornell) summary(cv.plsRglm(Y~.,data=Cornell, nt=6,K=12,NK=1,keepfolds=FALSE,keepdataY=TRUE,modele="pls",verbose=FALSE),MClassed=TRUE)#> ____************************************************____ #> #> Model: pls #> #> ____Component____ 1 ____ #> ____Component____ 2 ____ #> ____Component____ 3 ____ #> ____Component____ 4 ____ #> ____Component____ 5 ____ #> ____Component____ 6 ____ #> ____Predicting X without NA neither in X or Y____ #> ****________________________________________________**** #> #> #> NK: 1#> [[1]] #> AIC MissClassed CV_MissClassed Q2cum_Y LimQ2_Y Q2_Y #> Nb_Comp_0 82.01205 12 NA NA NA NA #> Nb_Comp_1 53.15173 12 12 0.8809146 0.0975 0.8809146 #> Nb_Comp_2 41.08283 12 12 0.8619560 0.0975 -0.1592015 #> Nb_Comp_3 32.06411 12 12 0.7471041 0.0975 -0.8319956 #> Nb_Comp_4 33.76477 12 12 -0.2159389 0.0975 -3.8080607 #> Nb_Comp_5 33.34373 12 12 -5.9182568 0.0975 -4.6896417 #> Nb_Comp_6 35.25533 12 NA NA 0.0975 NA #> PRESS_Y RSS_Y R2_Y AIC.std DoF.dof sigmahat.dof #> Nb_Comp_0 NA 467.796667 NA 37.010388 1.000000 6.5212706 #> Nb_Comp_1 55.70774 35.742486 0.9235940 8.150064 2.740749 1.8665281 #> Nb_Comp_2 41.43274 11.066606 0.9763431 -3.918831 5.085967 1.1825195 #> Nb_Comp_3 20.27397 4.418081 0.9905556 -12.937550 5.121086 0.7488308 #> Nb_Comp_4 21.24240 4.309235 0.9907882 -11.236891 5.103312 0.7387162 #> Nb_Comp_5 24.51801 3.521924 0.9924713 -11.657929 6.006316 0.7096382 #> Nb_Comp_6 NA 3.496074 0.9925265 -9.746328 7.000002 0.7633343 #> AIC.dof BIC.dof GMDL.dof DoF.naive sigmahat.naive AIC.naive #> Nb_Comp_0 46.0708838 47.7893514 27.59461 1 6.5212706 46.0708838 #> Nb_Comp_1 4.5699686 4.9558156 21.34020 2 1.8905683 4.1699567 #> Nb_Comp_2 2.1075461 2.3949331 27.40202 3 1.1088836 1.5370286 #> Nb_Comp_3 0.8467795 0.9628191 24.40842 4 0.7431421 0.7363469 #> Nb_Comp_4 0.8232505 0.9357846 24.23105 5 0.7846050 0.8721072 #> Nb_Comp_5 0.7976101 0.9198348 28.21184 6 0.7661509 0.8804809 #> Nb_Comp_6 0.9711322 1.1359501 33.18348 7 0.8361907 1.1070902 #> BIC.naive GMDL.naive #> Nb_Comp_0 47.7893514 27.59461 #> Nb_Comp_1 4.4588195 18.37545 #> Nb_Comp_2 1.6860917 17.71117 #> Nb_Comp_3 0.8256118 19.01033 #> Nb_Comp_4 0.9964867 24.16510 #> Nb_Comp_5 1.0227979 28.64206 #> Nb_Comp_6 1.3048716 33.63927 #> #> attr(,"class") #> [1] "summary.cv.plsRmodel"data(aze_compl) summary(cv.plsR(y~.,data=aze_compl,nt=10,K=8,modele="pls",verbose=FALSE), MClassed=TRUE,verbose=FALSE)#> [[1]] #> AIC MissClassed CV_MissClassed Q2cum_Y LimQ2_Y Q2_Y #> Nb_Comp_0 154.6179 49 NA NA NA NA #> Nb_Comp_1 126.4083 27 47 -0.1425746 0.0975 -0.1425746 #> Nb_Comp_2 119.3375 25 45 -0.7898945 0.0975 -0.5665450 #> Nb_Comp_3 114.2313 27 41 -2.3188777 0.0975 -0.8542309 #> Nb_Comp_4 112.3463 23 48 -6.4360193 0.0975 -1.2405223 #> Nb_Comp_5 113.2362 22 51 -17.0653150 0.0975 -1.4294336 #> Nb_Comp_6 114.7620 21 51 -44.8868490 0.0975 -1.5400525 #> Nb_Comp_7 116.5264 20 49 -118.0271913 0.0975 -1.5939282 #> Nb_Comp_8 118.4601 20 49 -311.0990873 0.0975 -1.6220823 #> Nb_Comp_9 120.4452 19 49 -817.6297833 0.0975 -1.6229804 #> Nb_Comp_10 122.4395 19 49 -2152.2071327 0.0975 -1.6302575 #> PRESS_Y RSS_Y R2_Y AIC.std DoF.dof sigmahat.dof AIC.dof #> Nb_Comp_0 NA 25.91346 NA 298.1344 1.00000 0.5015845 0.2540061 #> Nb_Comp_1 29.60806 19.38086 0.2520929 269.9248 22.55372 0.4848429 0.2883114 #> Nb_Comp_2 30.36099 17.76209 0.3145613 262.8540 27.31542 0.4781670 0.2908950 #> Nb_Comp_3 32.93501 16.58896 0.3598323 257.7478 30.52370 0.4719550 0.2902572 #> Nb_Comp_4 37.16794 15.98071 0.3833049 255.8628 34.00000 0.4744263 0.3008285 #> Nb_Comp_5 38.82406 15.81104 0.3898523 256.7527 34.00000 0.4719012 0.2976347 #> Nb_Comp_6 40.16087 15.73910 0.3926285 258.2785 34.00000 0.4708264 0.2962804 #> Nb_Comp_7 40.82609 15.70350 0.3940024 260.0429 33.71066 0.4693382 0.2937976 #> Nb_Comp_8 41.17586 15.69348 0.3943888 261.9766 34.00000 0.4701436 0.2954217 #> Nb_Comp_9 41.16370 15.69123 0.3944758 263.9617 33.87284 0.4696894 0.2945815 #> Nb_Comp_10 41.27197 15.69037 0.3945088 265.9560 34.00000 0.4700970 0.2953632 #> BIC.dof GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive #> Nb_Comp_0 0.2604032 -67.17645 1 0.5015845 0.2540061 0.2604032 #> Nb_Comp_1 0.4231184 -53.56607 2 0.4358996 0.1936625 0.2033251 #> Nb_Comp_2 0.4496983 -52.42272 3 0.4193593 0.1809352 0.1943501 #> Nb_Comp_3 0.4631316 -51.93343 4 0.4072955 0.1722700 0.1891422 #> Nb_Comp_4 0.4954133 -50.37079 5 0.4017727 0.1691819 0.1897041 #> Nb_Comp_5 0.4901536 -50.65724 6 0.4016679 0.1706451 0.1952588 #> Nb_Comp_6 0.4879234 -50.78005 7 0.4028135 0.1731800 0.2020601 #> Nb_Comp_7 0.4826103 -51.05525 8 0.4044479 0.1761610 0.2094352 #> Nb_Comp_8 0.4865092 -50.85833 9 0.4064413 0.1794902 0.2172936 #> Nb_Comp_9 0.4845867 -50.95616 10 0.4085682 0.1829787 0.2254232 #> Nb_Comp_10 0.4864128 -50.86368 11 0.4107477 0.1865584 0.2337468 #> GMDL.naive #> Nb_Comp_0 -67.17645 #> Nb_Comp_1 -79.67755 #> Nb_Comp_2 -81.93501 #> Nb_Comp_3 -83.31503 #> Nb_Comp_4 -83.23369 #> Nb_Comp_5 -81.93513 #> Nb_Comp_6 -80.42345 #> Nb_Comp_7 -78.87607 #> Nb_Comp_8 -77.31942 #> Nb_Comp_9 -75.80069 #> Nb_Comp_10 -74.33325 #> attr(,"computed_nt") #> [1] 10 #> #> attr(,"class") #> [1] "summary.cv.plsRmodel"summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,modele="pls",verbose=FALSE), MClassed=TRUE,verbose=FALSE)#> [[1]] #> AIC MissClassed CV_MissClassed Q2cum_Y LimQ2_Y Q2_Y #> Nb_Comp_0 154.6179 49 NA NA NA NA #> Nb_Comp_1 126.4083 27 51 -0.1849295 0.0975 -0.1849295 #> Nb_Comp_2 119.3375 25 44 -0.7605294 0.0975 -0.4857672 #> Nb_Comp_3 114.2313 27 45 -2.0286611 0.0975 -0.7203127 #> Nb_Comp_4 112.3463 23 51 -4.9329591 0.0975 -0.9589379 #> Nb_Comp_5 113.2362 22 51 -11.0704703 0.0975 -1.0344772 #> Nb_Comp_6 114.7620 21 48 -23.8046479 0.0975 -1.0549860 #> Nb_Comp_7 116.5264 20 48 -49.5975576 0.0975 -1.0398418 #> Nb_Comp_8 118.4601 20 48 -102.2128688 0.0975 -1.0398785 #> Nb_Comp_9 120.4452 19 48 -208.9148962 0.0975 -1.0338055 #> Nb_Comp_10 122.4395 19 48 -425.8897655 0.0975 -1.0336325 #> PRESS_Y RSS_Y R2_Y AIC.std DoF.dof sigmahat.dof AIC.dof #> Nb_Comp_0 NA 25.91346 NA 298.1344 1.00000 0.5015845 0.2540061 #> Nb_Comp_1 30.70563 19.38086 0.2520929 269.9248 22.55372 0.4848429 0.2883114 #> Nb_Comp_2 28.79545 17.76209 0.3145613 262.8540 27.31542 0.4781670 0.2908950 #> Nb_Comp_3 30.55635 16.58896 0.3598323 257.7478 30.52370 0.4719550 0.2902572 #> Nb_Comp_4 32.49675 15.98071 0.3833049 255.8628 34.00000 0.4744263 0.3008285 #> Nb_Comp_5 32.51238 15.81104 0.3898523 256.7527 34.00000 0.4719012 0.2976347 #> Nb_Comp_6 32.49147 15.73910 0.3926285 258.2785 34.00000 0.4708264 0.2962804 #> Nb_Comp_7 32.10527 15.70350 0.3940024 260.0429 33.71066 0.4693382 0.2937976 #> Nb_Comp_8 32.03322 15.69348 0.3943888 261.9766 34.00000 0.4701436 0.2954217 #> Nb_Comp_9 31.91749 15.69123 0.3944758 263.9617 33.87284 0.4696894 0.2945815 #> Nb_Comp_10 31.91019 15.69037 0.3945088 265.9560 34.00000 0.4700970 0.2953632 #> BIC.dof GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive #> Nb_Comp_0 0.2604032 -67.17645 1 0.5015845 0.2540061 0.2604032 #> Nb_Comp_1 0.4231184 -53.56607 2 0.4358996 0.1936625 0.2033251 #> Nb_Comp_2 0.4496983 -52.42272 3 0.4193593 0.1809352 0.1943501 #> Nb_Comp_3 0.4631316 -51.93343 4 0.4072955 0.1722700 0.1891422 #> Nb_Comp_4 0.4954133 -50.37079 5 0.4017727 0.1691819 0.1897041 #> Nb_Comp_5 0.4901536 -50.65724 6 0.4016679 0.1706451 0.1952588 #> Nb_Comp_6 0.4879234 -50.78005 7 0.4028135 0.1731800 0.2020601 #> Nb_Comp_7 0.4826103 -51.05525 8 0.4044479 0.1761610 0.2094352 #> Nb_Comp_8 0.4865092 -50.85833 9 0.4064413 0.1794902 0.2172936 #> Nb_Comp_9 0.4845867 -50.95616 10 0.4085682 0.1829787 0.2254232 #> Nb_Comp_10 0.4864128 -50.86368 11 0.4107477 0.1865584 0.2337468 #> GMDL.naive #> Nb_Comp_0 -67.17645 #> Nb_Comp_1 -79.67755 #> Nb_Comp_2 -81.93501 #> Nb_Comp_3 -83.31503 #> Nb_Comp_4 -83.23369 #> Nb_Comp_5 -81.93513 #> Nb_Comp_6 -80.42345 #> Nb_Comp_7 -78.87607 #> Nb_Comp_8 -77.31942 #> Nb_Comp_9 -75.80069 #> Nb_Comp_10 -74.33325 #> #> attr(,"class") #> [1] "summary.cv.plsRmodel"summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8, modele="pls-glm-family", family=gaussian(),verbose=FALSE), MClassed=TRUE,verbose=FALSE)#> [[1]] #> AIC BIC MissClassed CV_MissClassed Q2Chisqcum_Y limQ2 #> Nb_Comp_0 154.6179 159.9067 49 NA NA NA #> Nb_Comp_1 126.4083 134.3415 27 55 -0.2203592 0.0975 #> Nb_Comp_2 119.2021 129.7796 28 52 -1.0736014 0.0975 #> Nb_Comp_3 113.9553 127.1773 26 46 -3.3535280 0.0975 #> Nb_Comp_4 112.4466 128.3130 25 50 -9.5982958 0.0975 #> Nb_Comp_5 113.2280 131.7387 23 50 -25.3925882 0.0975 #> Nb_Comp_6 114.7095 135.8646 21 50 -64.9143258 0.0975 #> Nb_Comp_7 116.5144 140.3139 20 49 -163.8069642 0.0975 #> Nb_Comp_8 118.4615 144.9054 20 47 -410.7903119 0.0975 #> Nb_Comp_9 120.4453 149.5336 19 48 -1030.1669920 0.0975 #> Nb_Comp_10 122.4403 154.1729 19 48 -2587.3739041 0.0975 #> Q2Chisq_Y PREChi2_Pearson_Y Chi2_Pearson_Y RSS_Y R2_Y #> Nb_Comp_0 NA NA 25.91346 25.91346 NA #> Nb_Comp_1 -0.2203592 31.62373 19.38086 19.38086 0.2520929 #> Nb_Comp_2 -0.6991730 32.93144 17.73898 17.73898 0.3154532 #> Nb_Comp_3 -1.0995010 37.24300 16.54501 16.54501 0.3615285 #> Nb_Comp_4 -1.4344154 40.27742 15.99613 15.99613 0.3827095 #> Nb_Comp_5 -1.4902672 39.83464 15.80978 15.80978 0.3899009 #> Nb_Comp_6 -1.4974559 39.48423 15.73116 15.73116 0.3929346 #> Nb_Comp_7 -1.5003209 39.33296 15.70168 15.70168 0.3940726 #> Nb_Comp_8 -1.4986220 39.23255 15.69369 15.69369 0.3943807 #> Nb_Comp_9 -1.5041070 39.29869 15.69125 15.69125 0.3944749 #> Nb_Comp_10 -1.5101404 39.38724 15.69049 15.69049 0.3945043 #> #> attr(,"class") #> [1] "summary.cv.plsRglmmodel"summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8, modele="pls-glm-logistic", verbose=FALSE),MClassed=TRUE,verbose=FALSE)#> [[1]] #> AIC BIC MissClassed CV_MissClassed Q2Chisqcum_Y limQ2 #> Nb_Comp_0 145.8283 148.4727 49 NA NA NA #> Nb_Comp_1 118.1398 123.4285 28 47 -7.866502e-01 0.0975 #> Nb_Comp_2 109.9553 117.8885 26 45 -5.859374e+00 0.0975 #> Nb_Comp_3 105.1591 115.7366 22 43 -2.548634e+02 0.0975 #> Nb_Comp_4 103.8382 117.0601 21 43 -3.048078e+04 0.0975 #> Nb_Comp_5 104.7338 120.6001 21 48 -4.528321e+06 0.0975 #> Nb_Comp_6 105.6770 124.1878 21 46 -9.277725e+08 0.0975 #> Nb_Comp_7 107.2828 128.4380 20 46 -1.850540e+11 0.0975 #> Nb_Comp_8 109.0172 132.8167 22 49 -3.260386e+13 0.0975 #> Nb_Comp_9 110.9354 137.3793 21 46 -5.797175e+15 0.0975 #> Nb_Comp_10 112.9021 141.9904 20 45 -1.173899e+18 0.0975 #> Q2Chisq_Y PREChi2_Pearson_Y Chi2_Pearson_Y RSS_Y R2_Y #> Nb_Comp_0 NA NA 104.00000 25.91346 NA #> Nb_Comp_1 -0.7866502 185.8116 100.53823 19.32272 0.2543365 #> Nb_Comp_2 -2.8392372 385.9901 99.17955 17.33735 0.3309519 #> Nb_Comp_3 -36.3012794 3699.5243 123.37836 15.58198 0.3986915 #> Nb_Comp_4 -118.1330240 14698.4369 114.77551 15.14046 0.4157299 #> Nb_Comp_5 -147.5583003 17050.8547 105.35382 15.08411 0.4179043 #> Nb_Comp_6 -203.8821899 21585.1208 98.87767 14.93200 0.4237744 #> Nb_Comp_7 -198.4605861 19722.1980 97.04072 14.87506 0.4259715 #> Nb_Comp_8 -175.1855949 17097.1766 98.90110 14.84925 0.4269676 #> Nb_Comp_9 -176.8064232 17585.2507 100.35563 14.84317 0.4272022 #> Nb_Comp_10 -201.4949885 20321.5120 102.85214 14.79133 0.4292027 #> #> attr(,"class") #> [1] "summary.cv.plsRglmmodel"summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8, modele="pls-glm-family", family=binomial(),verbose=FALSE), MClassed=TRUE,verbose=FALSE)#> [[1]] #> AIC BIC MissClassed CV_MissClassed Q2Chisqcum_Y limQ2 #> Nb_Comp_0 145.8283 148.4727 49 NA NA NA #> Nb_Comp_1 118.1398 123.4285 28 44 -1.205515e+00 0.0975 #> Nb_Comp_2 109.9553 117.8885 26 53 -9.473232e+00 0.0975 #> Nb_Comp_3 105.1591 115.7366 22 48 -3.164492e+02 0.0975 #> Nb_Comp_4 103.8382 117.0601 21 51 -2.180828e+04 0.0975 #> Nb_Comp_5 104.7338 120.6001 21 49 -1.823483e+06 0.0975 #> Nb_Comp_6 105.6770 124.1878 21 46 -1.936616e+08 0.0975 #> Nb_Comp_7 107.2828 128.4380 20 47 -3.485931e+10 0.0975 #> Nb_Comp_8 109.0172 132.8167 22 48 -1.020438e+13 0.0975 #> Nb_Comp_9 110.9354 137.3793 21 50 -4.084975e+15 0.0975 #> Nb_Comp_10 112.9021 141.9904 20 50 -1.782231e+18 0.0975 #> Q2Chisq_Y PREChi2_Pearson_Y Chi2_Pearson_Y RSS_Y R2_Y #> Nb_Comp_0 NA NA 104.00000 25.91346 NA #> Nb_Comp_1 -1.205515 229.3735 100.53823 19.32272 0.2543365 #> Nb_Comp_2 -3.748656 477.4215 99.17955 17.33735 0.3309519 #> Nb_Comp_3 -29.310534 3006.1853 123.37836 15.58198 0.3986915 #> Nb_Comp_4 -67.701635 8476.2950 114.77551 15.14046 0.4157299 #> Nb_Comp_5 -82.610442 9596.4312 105.35382 15.08411 0.4179043 #> Nb_Comp_6 -105.204202 11189.0181 98.87767 14.93200 0.4237744 #> Nb_Comp_7 -179.001112 17798.0905 97.04072 14.87506 0.4259715 #> Nb_Comp_8 -291.730492 28406.7771 98.90110 14.84925 0.4269676 #> Nb_Comp_9 -399.315712 39591.6641 100.35563 14.84317 0.4272022 #> Nb_Comp_10 -435.289335 43784.0908 102.85214 14.79133 0.4292027 #> #> attr(,"class") #> [1] "summary.cv.plsRglmmodel"if(require(chemometrics)){ data(hyptis) hyptis yhyptis <- factor(hyptis$Group,ordered=TRUE) Xhyptis <- as.data.frame(hyptis[,c(1:6)]) options(contrasts = c("contr.treatment", "contr.poly")) modpls2 <- plsRglm(yhyptis,Xhyptis,6,modele="pls-glm-polr") modpls2$Coeffsmodel_vals modpls2$InfCrit modpls2$Coeffs modpls2$std.coeffs table(yhyptis,predict(modpls2$FinalModel,type="class")) modpls3 <- PLS_glm(yhyptis[-c(1,2,3)],Xhyptis[-c(1,2,3),],3,modele="pls-glm-polr", dataPredictY=Xhyptis[c(1,2,3),],verbose=FALSE) summary(cv.plsRglm(factor(Group,ordered=TRUE)~.,data=hyptis[,-c(7,8)],nt=4,K=10, random=TRUE,modele="pls-glm-polr",keepcoeffs=TRUE,verbose=FALSE), MClassed=TRUE,verbose=FALSE) }#> ____************************************************____ #> #> Model: pls-glm-polr #> Method: logistic #> #> ____Component____ 1 ____ #> ____Component____ 2 ____ #> ____Component____ 3 ____ #> ____Component____ 4 ____ #> ____Component____ 5 ____ #> ____Component____ 6 ____ #> ____Predicting X without NA neither in X nor in Y____ #> ****________________________________________________**** #>#> Warning: glm.fit: algorithm did not converge#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> Warning: glm.fit: algorithm did not converge#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> Warning: glm.fit: algorithm did not converge#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> Warning: glm.fit: algorithm did not converge#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> Warning: glm.fit: algorithm did not converge#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> Warning: glm.fit: algorithm did not converge#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#> [[1]] #> AIC BIC MissClassed CV_MissClassed Q2Chisqcum_Y limQ2 #> Nb_Comp_0 86.87461 91.07820 20 NA NA NA #> Nb_Comp_1 72.73191 78.33670 13 14 -4.821331e+00 0.0975 #> Nb_Comp_2 71.10453 78.11052 12 13 -7.469665e+02 0.0975 #> Nb_Comp_3 66.56795 74.97514 11 15 -1.240289e+04 0.0975 #> Nb_Comp_4 67.29294 77.10133 10 13 -1.430738e+05 0.0975 #> Q2Chisq_Y PREChi2_Pearson_Y Chi2_Pearson_Y #> Nb_Comp_0 NA NA 60.00011 #> Nb_Comp_1 -4.821331 349.2805 30.46894 #> Nb_Comp_2 -127.487203 3914.8688 27.68501 #> Nb_Comp_3 -15.583480 459.1139 24.52007 #> Nb_Comp_4 -10.534677 282.8311 24.43497 #> #> attr(,"class") #> [1] "summary.cv.plsRglmmodel"# }