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://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/47/
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 0.9711321 1.1359501 33.18347 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 45 -0.1267597 0.0975 -0.1267597
#> Nb_Comp_2 119.3375 25 50 -0.7883936 0.0975 -0.5872005
#> Nb_Comp_3 114.2313 27 44 -2.4260707 0.0975 -0.9157252
#> Nb_Comp_4 112.3463 23 48 -6.6030021 0.0975 -1.2191609
#> Nb_Comp_5 113.2362 22 52 -16.9740350 0.0975 -1.3640708
#> Nb_Comp_6 114.7620 21 53 -42.5282283 0.0975 -1.4217283
#> Nb_Comp_7 116.5264 20 52 -104.3561948 0.0975 -1.4204108
#> Nb_Comp_8 118.4601 20 52 -253.9485578 0.0975 -1.4198725
#> Nb_Comp_9 120.4452 19 52 -617.9161650 0.0975 -1.4276119
#> Nb_Comp_10 122.4395 19 53 -1507.0570241 0.0975 -1.4366095
#> 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 29.19824 19.38086 0.2520929 269.9248 22.55372 0.4848429 0.2883114
#> Nb_Comp_2 30.76131 17.76209 0.3145613 262.8540 27.31542 0.4781670 0.2908950
#> Nb_Comp_3 34.02728 16.58896 0.3598323 257.7478 30.52370 0.4719550 0.2902572
#> Nb_Comp_4 36.81358 15.98071 0.3833049 255.8628 34.00000 0.4744263 0.3008285
#> Nb_Comp_5 37.77952 15.81104 0.3898523 256.7527 34.00000 0.4719012 0.2976347
#> Nb_Comp_6 38.29004 15.73910 0.3926285 258.2785 34.00000 0.4708264 0.2962804
#> Nb_Comp_7 38.09509 15.70350 0.3940024 260.0429 33.71066 0.4693382 0.2937976
#> Nb_Comp_8 38.00046 15.69348 0.3943888 261.9766 34.00000 0.4701436 0.2954217
#> Nb_Comp_9 38.09768 15.69123 0.3944758 263.9617 33.87284 0.4696894 0.2945815
#> Nb_Comp_10 38.23340 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 52 -0.2605261 0.0975 -0.2605261
#> Nb_Comp_2 119.3375 25 49 -1.2082024 0.0975 -0.7518101
#> Nb_Comp_3 114.2313 27 48 -3.4138555 0.0975 -0.9988456
#> Nb_Comp_4 112.3463 23 52 -9.5433577 0.0975 -1.3886957
#> Nb_Comp_5 113.2362 22 48 -25.6668906 0.0975 -1.5292598
#> Nb_Comp_6 114.7620 21 48 -66.4979067 0.0975 -1.5311502
#> Nb_Comp_7 116.5264 20 49 -168.4398626 0.0975 -1.5102980
#> Nb_Comp_8 118.4601 20 48 -424.6365094 0.0975 -1.5120211
#> Nb_Comp_9 120.4452 19 49 -1069.4054719 0.0975 -1.5148347
#> Nb_Comp_10 122.4395 19 49 -2691.6606109 0.0975 -1.5155520
#> 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 32.66459 19.38086 0.2520929 269.9248 22.55372 0.4848429 0.2883114
#> Nb_Comp_2 33.95159 17.76209 0.3145613 262.8540 27.31542 0.4781670 0.2908950
#> Nb_Comp_3 35.50367 16.58896 0.3598323 257.7478 30.52370 0.4719550 0.2902572
#> Nb_Comp_4 39.62598 15.98071 0.3833049 255.8628 34.00000 0.4744263 0.3008285
#> Nb_Comp_5 40.41935 15.81104 0.3898523 256.7527 34.00000 0.4719012 0.2976347
#> Nb_Comp_6 40.02012 15.73910 0.3926285 258.2785 34.00000 0.4708264 0.2962804
#> Nb_Comp_7 39.50983 15.70350 0.3940024 260.0429 33.71066 0.4693382 0.2937976
#> Nb_Comp_8 39.44751 15.69348 0.3943888 261.9766 34.00000 0.4701436 0.2954217
#> Nb_Comp_9 39.46651 15.69123 0.3944758 263.9617 33.87284 0.4696894 0.2945815
#> Nb_Comp_10 39.47210 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 NA NA 0.0975
#> Nb_Comp_2 119.2021 129.7796 28 NA NA 0.0975
#> Nb_Comp_3 113.9553 127.1773 26 NA NA 0.0975
#> Nb_Comp_4 112.4466 128.3130 25 NA NA 0.0975
#> Nb_Comp_5 113.2280 131.7387 23 NA NA 0.0975
#> Nb_Comp_6 114.7095 135.8646 21 NA NA 0.0975
#> Nb_Comp_7 116.5144 140.3139 20 NA NA 0.0975
#> Nb_Comp_8 118.4615 144.9054 20 NA NA 0.0975
#> Nb_Comp_9 120.4453 149.5336 19 NA NA 0.0975
#> Nb_Comp_10 122.4403 154.1729 19 NA NA 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 NA NA 19.38086 19.38086 0.2520929
#> Nb_Comp_2 NA NA 17.73898 17.73898 0.3154532
#> Nb_Comp_3 NA NA 16.54501 16.54501 0.3615285
#> Nb_Comp_4 NA NA 15.99613 15.99613 0.3827095
#> Nb_Comp_5 NA NA 15.80978 15.80978 0.3899009
#> Nb_Comp_6 NA NA 15.73116 15.73116 0.3929346
#> Nb_Comp_7 NA NA 15.70168 15.70168 0.3940726
#> Nb_Comp_8 NA NA 15.69369 15.69369 0.3943807
#> Nb_Comp_9 NA NA 15.69125 15.69125 0.3944749
#> Nb_Comp_10 NA NA 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 NA NA 0.0975
#> Nb_Comp_2 109.9553 117.8885 26 NA NA 0.0975
#> Nb_Comp_3 105.1591 115.7366 22 NA NA 0.0975
#> Nb_Comp_4 103.8382 117.0601 21 NA NA 0.0975
#> Nb_Comp_5 104.7338 120.6001 21 NA NA 0.0975
#> Nb_Comp_6 105.6770 124.1878 21 NA NA 0.0975
#> Nb_Comp_7 107.2828 128.4380 20 NA NA 0.0975
#> Nb_Comp_8 109.0172 132.8167 22 NA NA 0.0975
#> Nb_Comp_9 110.9354 137.3793 21 NA NA 0.0975
#> Nb_Comp_10 112.9021 141.9904 20 NA NA 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 NA NA 100.53823 19.32272 0.2543365
#> Nb_Comp_2 NA NA 99.17955 17.33735 0.3309519
#> Nb_Comp_3 NA NA 123.37836 15.58198 0.3986915
#> Nb_Comp_4 NA NA 114.77551 15.14046 0.4157299
#> Nb_Comp_5 NA NA 105.35382 15.08411 0.4179043
#> Nb_Comp_6 NA NA 98.87767 14.93200 0.4237744
#> Nb_Comp_7 NA NA 97.04072 14.87506 0.4259715
#> Nb_Comp_8 NA NA 98.90110 14.84925 0.4269676
#> Nb_Comp_9 NA NA 100.35563 14.84317 0.4272022
#> Nb_Comp_10 NA NA 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 NA NA 0.0975
#> Nb_Comp_2 109.9553 117.8885 26 NA NA 0.0975
#> Nb_Comp_3 105.1591 115.7366 22 NA NA 0.0975
#> Nb_Comp_4 103.8382 117.0601 21 NA NA 0.0975
#> Nb_Comp_5 104.7338 120.6001 21 NA NA 0.0975
#> Nb_Comp_6 105.6770 124.1878 21 NA NA 0.0975
#> Nb_Comp_7 107.2828 128.4380 20 NA NA 0.0975
#> Nb_Comp_8 109.0172 132.8167 22 NA NA 0.0975
#> Nb_Comp_9 110.9354 137.3793 21 NA NA 0.0975
#> Nb_Comp_10 112.9021 141.9904 20 NA NA 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 NA NA 100.53823 19.32272 0.2543365
#> Nb_Comp_2 NA NA 99.17955 17.33735 0.3309519
#> Nb_Comp_3 NA NA 123.37836 15.58198 0.3986915
#> Nb_Comp_4 NA NA 114.77551 15.14046 0.4157299
#> Nb_Comp_5 NA NA 105.35382 15.08411 0.4179043
#> Nb_Comp_6 NA NA 98.87767 14.93200 0.4237744
#> Nb_Comp_7 NA NA 97.04072 14.87506 0.4259715
#> Nb_Comp_8 NA NA 98.90110 14.84925 0.4269676
#> Nb_Comp_9 NA NA 100.35563 14.84317 0.4272022
#> Nb_Comp_10 NA NA 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: 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: 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: 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 91.0782 20 NA NA NA
#> Nb_Comp_1 72.73191 78.3367 13 15 -5.092085 0.0975
#> Q2Chisq_Y PREChi2_Pearson_Y Chi2_Pearson_Y
#> Nb_Comp_0 NA NA 60.00011
#> Nb_Comp_1 -5.092085 365.5258 30.46894
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
# }