Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm models
Source: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.
Arguments
- pls_kfolds
an object computed using
cv.plsRglm- MClassed
should number of miss classed be computed ?
- verbose
should infos be displayed ?
Value
- list
table of fit statistics for first group partition
- list()
...
- list
table of fit statistics for last group partition
Note
Use summary and cv.plsRglm instead.
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
kfolds2coeff, kfolds2Pressind,
kfolds2Press, 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
# \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 1.1359502 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 50 -0.1644349 0.0975 -0.1644349
#> Nb_Comp_2 119.3375 25 51 -0.9368004 0.0975 -0.6632964
#> Nb_Comp_3 114.2313 27 49 -2.7588358 0.0975 -0.9407451
#> Nb_Comp_4 112.3463 23 51 -7.9590981 0.0975 -1.3834768
#> Nb_Comp_5 113.2362 22 50 -21.7176352 0.0975 -1.5357056
#> Nb_Comp_6 114.7620 21 50 -58.9588752 0.0975 -1.6393097
#> Nb_Comp_7 116.5264 20 47 -158.6242020 0.0975 -1.6622281
#> Nb_Comp_8 118.4601 20 49 -425.6996662 0.0975 -1.6731514
#> Nb_Comp_9 120.4452 19 49 -1141.4655676 0.0975 -1.6774466
#> Nb_Comp_10 122.4395 19 50 -3058.2149555 0.0975 -1.6777306
#> 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 30.17454 19.38086 0.2520929 269.9248 22.55372 0.4848429 0.2883114
#> Nb_Comp_2 32.23612 17.76209 0.3145613 262.8540 27.31542 0.4781670 0.2908950
#> Nb_Comp_3 34.47169 16.58896 0.3598323 257.7478 30.52370 0.4719550 0.2902572
#> Nb_Comp_4 39.53941 15.98071 0.3833049 255.8628 34.00000 0.4744263 0.3008285
#> Nb_Comp_5 40.52236 15.81104 0.3898523 256.7527 34.00000 0.4719012 0.2976347
#> Nb_Comp_6 41.73023 15.73910 0.3926285 258.2785 34.00000 0.4708264 0.2962804
#> Nb_Comp_7 41.90107 15.70350 0.3940024 260.0429 33.71066 0.4693382 0.2937976
#> Nb_Comp_8 41.97782 15.69348 0.3943888 261.9766 34.00000 0.4701436 0.2954217
#> Nb_Comp_9 42.01846 15.69123 0.3944758 263.9617 33.87284 0.4696894 0.2945815
#> Nb_Comp_10 42.01688 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 48 -0.1831188 0.0975 -0.1831188
#> Nb_Comp_2 119.3375 25 47 -0.8027725 0.0975 -0.5237460
#> Nb_Comp_3 114.2313 27 46 -2.3498032 0.0975 -0.8581397
#> Nb_Comp_4 112.3463 23 46 -6.0371280 0.0975 -1.1007586
#> Nb_Comp_5 113.2362 22 46 -14.5911203 0.0975 -1.2155516
#> Nb_Comp_6 114.7620 21 48 -33.9231748 0.0975 -1.2399401
#> Nb_Comp_7 116.5264 20 48 -77.7075380 0.0975 -1.2537338
#> Nb_Comp_8 118.4601 20 48 -176.8951415 0.0975 -1.2602046
#> Nb_Comp_9 120.4452 19 48 -401.7658972 0.0975 -1.2640635
#> Nb_Comp_10 122.4395 19 48 -912.7341538 0.0975 -1.2686483
#> 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 30.65870 19.38086 0.2520929 269.9248 22.55372 0.4848429 0.2883114
#> Nb_Comp_2 29.53151 17.76209 0.3145613 262.8540 27.31542 0.4781670 0.2908950
#> Nb_Comp_3 33.00444 16.58896 0.3598323 257.7478 30.52370 0.4719550 0.2902572
#> Nb_Comp_4 34.84940 15.98071 0.3833049 255.8628 34.00000 0.4744263 0.3008285
#> Nb_Comp_5 35.40608 15.81104 0.3898523 256.7527 34.00000 0.4719012 0.2976347
#> Nb_Comp_6 35.41578 15.73910 0.3926285 258.2785 34.00000 0.4708264 0.2962804
#> Nb_Comp_7 35.47174 15.70350 0.3940024 260.0429 33.71066 0.4693382 0.2937976
#> Nb_Comp_8 35.49311 15.69348 0.3943888 261.9766 34.00000 0.4701436 0.2954217
#> Nb_Comp_9 35.53104 15.69123 0.3944758 263.9617 33.87284 0.4696894 0.2945815
#> Nb_Comp_10 35.59788 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 47 -0.1453856 0.0975
#> Nb_Comp_2 119.2021 129.7796 28 47 -0.8099674 0.0975
#> Nb_Comp_3 113.9553 127.1773 26 48 -2.5559644 0.0975
#> Nb_Comp_4 112.4466 128.3130 25 49 -7.3670862 0.0975
#> Nb_Comp_5 113.2280 131.7387 23 49 -20.0581535 0.0975
#> Nb_Comp_6 114.7095 135.8646 21 49 -52.2274814 0.0975
#> Nb_Comp_7 116.5144 140.3139 20 50 -134.8843601 0.0975
#> Nb_Comp_8 118.4615 144.9054 20 49 -349.9646424 0.0975
#> Nb_Comp_9 120.4453 149.5336 19 48 -908.5700082 0.0975
#> Nb_Comp_10 122.4403 154.1729 19 48 -2354.9438614 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 -0.1453856 29.68091 19.38086 19.38086 0.2520929
#> Nb_Comp_2 -0.5802254 30.62613 17.73898 17.73898 0.3154532
#> Nb_Comp_3 -0.9646566 34.85100 16.54501 16.54501 0.3615285
#> Nb_Comp_4 -1.3529724 38.92994 15.99613 15.99613 0.3827095
#> Nb_Comp_5 -1.5167846 40.25882 15.80978 15.80978 0.3899009
#> Nb_Comp_6 -1.5276424 39.96147 15.73116 15.73116 0.3929346
#> Nb_Comp_7 -1.5528985 40.16007 15.70168 15.70168 0.3940726
#> Nb_Comp_8 -1.5828185 40.55458 15.69369 15.69369 0.3943807
#> Nb_Comp_9 -1.5916286 40.67222 15.69125 15.69125 0.3944749
#> Nb_Comp_10 -1.5901732 40.64306 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 43 -5.824077e-01 0.0975
#> Nb_Comp_2 109.9553 117.8885 26 42 -3.009528e+00 0.0975
#> Nb_Comp_3 105.1591 115.7366 22 40 -2.397657e+01 0.0975
#> Nb_Comp_4 103.8382 117.0601 21 42 -3.332401e+02 0.0975
#> Nb_Comp_5 104.7338 120.6001 21 44 -8.016615e+03 0.0975
#> Nb_Comp_6 105.6770 124.1878 21 47 -2.915479e+05 0.0975
#> Nb_Comp_7 107.2828 128.4380 20 47 -1.393612e+07 0.0975
#> Nb_Comp_8 109.0172 132.8167 22 47 -7.271549e+08 0.0975
#> Nb_Comp_9 110.9354 137.3793 21 46 -3.443722e+10 0.0975
#> Nb_Comp_10 112.9021 141.9904 20 46 -1.429540e+12 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 -0.5824077 164.5704 100.53823 19.32272 0.2543365
#> Nb_Comp_2 -1.5338150 254.7453 99.17955 17.33735 0.3309519
#> Nb_Comp_3 -5.2293034 617.8195 123.37836 15.58198 0.3986915
#> Nb_Comp_4 -12.3821465 1651.0673 114.77551 15.14046 0.4157299
#> Nb_Comp_5 -22.9875903 2753.1879 105.35382 15.08411 0.4179043
#> Nb_Comp_6 -35.3635495 3831.0388 98.87767 14.93200 0.4237744
#> Nb_Comp_7 -46.8002829 4726.3806 97.04072 14.87506 0.4259715
#> Nb_Comp_8 -51.1777103 5063.3625 98.90110 14.84925 0.4269676
#> Nb_Comp_9 -46.3588535 4683.8427 100.35563 14.84317 0.4272022
#> Nb_Comp_10 -40.5114777 4165.9105 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 43 -1.206801e+00 0.0975
#> Nb_Comp_2 109.9553 117.8885 26 49 -7.872226e+00 0.0975
#> Nb_Comp_3 105.1591 115.7366 22 50 -1.682392e+02 0.0975
#> Nb_Comp_4 103.8382 117.0601 21 53 -1.068885e+04 0.0975
#> Nb_Comp_5 104.7338 120.6001 21 54 -1.345857e+06 0.0975
#> Nb_Comp_6 105.6770 124.1878 21 50 -2.734945e+08 0.0975
#> Nb_Comp_7 107.2828 128.4380 20 47 -6.274124e+10 0.0975
#> Nb_Comp_8 109.0172 132.8167 22 50 -1.950143e+13 0.0975
#> Nb_Comp_9 110.9354 137.3793 21 51 -7.253979e+15 0.0975
#> Nb_Comp_10 112.9021 141.9904 20 49 -3.481376e+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 -1.206801 229.5073 100.53823 19.32272 0.2543365
#> Nb_Comp_2 -3.020402 404.2041 99.17955 17.33735 0.3309519
#> Nb_Comp_3 -18.075169 1891.8668 123.37836 15.58198 0.3986915
#> Nb_Comp_4 -62.164168 7793.0914 114.77551 15.14046 0.4157299
#> Nb_Comp_5 -124.900478 14450.2916 105.35382 15.08411 0.4179043
#> Nb_Comp_6 -202.212071 21409.1674 98.87767 14.93200 0.4237744
#> Nb_Comp_7 -228.405804 22683.1114 97.04072 14.87506 0.4259715
#> Nb_Comp_8 -309.823158 30162.5024 98.90110 14.84925 0.4269676
#> Nb_Comp_9 -370.971686 36788.4087 100.35563 14.84317 0.4272022
#> Nb_Comp_10 -478.926330 48163.3090 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)
}
#> Loading required package: chemometrics
#> Warning: there is no package called ‘chemometrics’
# }