Computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.

Source: R/kfolds2Chisqind.R

This function computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.

kfolds2Chisqind(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression glm model

Value

list

Individual PChisq vs number of components for the first group partition

list()

...

list

Individual PChisq vs number of components for the last group partition

Note

Use cv.plsRglm to create k-fold cross validated partial least squares regression glm models.

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

See also

kfolds2coeff, kfolds2Press, kfolds2Pressind, kfolds2Chisq, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross-validation.

Examples

# \donttest{
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
bbb <- cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=16,verbose=FALSE)
bbb2 <- cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=5,verbose=FALSE)
kfolds2Chisqind(bbb)
#> [[1]]
#> [[1]][[1]]
#>       [,1]    [,2]     [,3]
#> 1 24.52456 11.3923 6.837075
#> 
#> [[1]][[2]]
#>       [,1]        [,2]        [,3]
#> 2 4.124502 0.007566232 0.005169207
#> 
#> [[1]][[3]]
#>       [,1]     [,2]     [,3]
#> 3 1.551301 1.917305 1.907019
#> 
#> [[1]][[4]]
#>       [,1]     [,2]        [,3]
#> 4 12.26717 1.278425 0.003572679
#> 
#> [[1]][[5]]
#>       [,1]     [,2]     [,3]
#> 5 4.315406 5.393862 4.249662
#> 
#> [[1]][[6]]
#>       [,1]     [,2]     [,3]
#> 6 6.209332 3.528493 1.696534
#> 
#> [[1]][[7]]
#>         [,1]     [,2]      [,3]
#> 7 0.08812214 0.256731 0.1887149
#> 
#> [[1]][[8]]
#>        [,1]     [,2]     [,3]
#> 8 0.9442012 0.240037 0.359011
#> 
#> [[1]][[9]]
#>        [,1]       [,2]       [,3]
#> 9 0.2203272 0.02253865 0.03724274
#> 
#> [[1]][[10]]
#>         [,1]       [,2]       [,3]
#> 10 0.6034613 0.05144812 0.01893424
#> 
#> [[1]][[11]]
#>         [,1]      [,2]      [,3]
#> 11 0.8275623 0.4409475 0.7467284
#> 
#> [[1]][[12]]
#>          [,1]         [,2]     [,3]
#> 12 0.03180524 1.455262e-05 4.794104
#> 
#> 
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 5.825877 4.725472 3.883973
#> 
#> [[1]][[2]]
#> [1] 30.892452  9.685118  5.997163
#> 
#> [[1]][[3]]
#> [1] 6.403230 3.555880 5.588619
#> 
#> [[1]][[4]]
#> [1] 1.1270093 0.5523745 0.5294297
#> 
#> [[1]][[5]]
#> [1] 12.3290316  1.1463315  0.2526569
#> 
#> 
rm(list=c("XCornell","yCornell","bbb","bbb2"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb <- cv.plsRglm(object=ypine,dataX=Xpine,nt=4,modele="pls-glm-gaussian",verbose=FALSE)
bbb2 <- cv.plsRglm(object=ypine,dataX=Xpine,nt=10,modele="pls-glm-gaussian",K=10,verbose=FALSE)
kfolds2Chisqind(bbb)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.836167 2.171958 2.900816 3.383174
#> 
#> [[1]][[2]]
#> [1] 2.265851 1.880170 1.424197 1.222636
#> 
#> [[1]][[3]]
#> [1] 3.750548 3.992624 3.457277 3.445154
#> 
#> [[1]][[4]]
#> [1] 1.481057 2.508055 2.534248 3.022888
#> 
#> [[1]][[5]]
#> [1] 3.873793 2.379961 2.029131 2.035533
#> 
#> 
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#>  [1] 1.874108 1.011835 1.004399 1.529960 1.607138 1.512919 1.743330 1.693070
#>  [9] 1.668737 1.669246
#> 
#> [[1]][[2]]
#>  [1] 0.5217522 1.3551071 1.2111218 1.4370478 1.5116616 2.1022189 2.1317979
#>  [8] 1.9967580 1.9700226 1.9681641
#> 
#> [[1]][[3]]
#>  [1] 0.9979281 1.2501863 1.0512863 0.8472644 0.9234442 0.9745070 0.9474871
#>  [8] 0.9671889 0.9993740 0.9871123
#> 
#> [[1]][[4]]
#>  [1] 2.519977 2.206042 1.952335 1.827331 1.885652 1.856834 1.727411 1.676181
#>  [9] 1.670902 1.664539
#> 
#> [[1]][[5]]
#>  [1] 0.9321451 1.0740008 1.1154523 1.6694813 1.9219940 1.9060085 1.8813871
#>  [8] 1.9653038 1.9514983 1.9565490
#> 
#> [[1]][[6]]
#>  [1] 0.3778648 0.3687811 0.3809376 0.3492512 0.3665322 0.3751166 0.3365295
#>  [8] 0.3371681 0.3106785 0.3055863
#> 
#> [[1]][[7]]
#>  [1] 1.1829590 0.4605101 0.3786150 0.6044597 0.6084287 0.5474187 0.4605802
#>  [8] 0.5076396 0.4965515 0.4855881
#> 
#> [[1]][[8]]
#>  [1] 1.3649672 0.4581475 0.4310822 0.4633675 0.6159427 0.7767888 0.8049489
#>  [8] 1.0173579 0.9380259 0.9366543
#> 
#> [[1]][[9]]
#>  [1] 2.808303 2.950943 2.879684 2.822780 2.778613 2.811510 2.691464 2.638846
#>  [9] 2.654725 2.654184
#> 
#> [[1]][[10]]
#>  [1] 0.4520341 0.2324618 0.3809298 0.2962025 0.2807686 0.3081371 0.2907263
#>  [8] 0.2998499 0.3020013 0.3020402
#> 
#> 
                  
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls",K=10,verbose=FALSE)
kfolds2Pressind(bbbNA)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.513312 3.052953 3.337869 3.093812 3.149831 3.323122 2.929652 3.200915
#> [9] 3.152576
#> 
#> [[1]][[2]]
#> [1] 1.1733871 1.7555732 1.4226946 0.6836313 0.5431520 0.4843809 0.6276720
#> [8] 0.8598455 0.7421705
#> 
#> [[1]][[3]]
#> [1] 3.220658 2.205590 2.303067 1.973497 1.807882 1.755839 1.689369 1.837478
#> [9] 2.147036
#> 
#> [[1]][[4]]
#> [1]  2.028142  3.556218  9.316032  2.991170  3.970424  3.311085  3.769649
#> [8]  2.969452 21.511957
#> 
#> [[1]][[5]]
#> [1] 0.08983156 0.15380533 0.33759110 0.54674379 0.33170610 0.28865037 0.28250955
#> [8] 0.39500298 0.41466648
#> 
#> [[1]][[6]]
#> [1] 0.05182149 0.41743984 0.63520907 0.23623211 0.16353036 0.13451805 0.17102474
#> [8] 0.18293842 0.17436122
#> 
#> [[1]][[7]]
#> [1] 2.884425 2.994256 2.563328 2.528139 2.679789 2.455888 2.574421 3.270582
#> [9] 4.062107
#> 
#> [[1]][[8]]
#> [1] 1.5314888 0.9073527 0.8546632 0.9492293 1.0062567 1.2156124 1.2027054
#> [8] 1.2330194 1.3702081
#> 
#> [[1]][[9]]
#> [1] 0.7060897 0.5736736 0.9937846 0.6851302 0.6394279 0.5405938 0.5467194
#> [8] 0.5027723 0.4319827
#> 
#> [[1]][[10]]
#> [1] 1.4192751 1.0023379 0.7331192 0.1370833 0.1509492 0.1268596 0.2293369
#> [8] 0.2057744 0.1809544
#> 
#> 
kfolds2Chisqind(bbbNA)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.513312 3.052953 3.337869 3.093812 3.149831 3.323122 2.929652 3.200915
#> [9] 3.152576
#> 
#> [[1]][[2]]
#> [1] 1.1733871 1.7555732 1.4226946 0.6836313 0.5431520 0.4843809 0.6276720
#> [8] 0.8598455 0.7421705
#> 
#> [[1]][[3]]
#> [1] 3.220658 2.205590 2.303067 1.973497 1.807882 1.755839 1.689369 1.837478
#> [9] 2.147036
#> 
#> [[1]][[4]]
#> [1]  2.028142  3.556218  9.316032  2.991170  3.970424  3.311085  3.769649
#> [8]  2.969452 21.511957
#> 
#> [[1]][[5]]
#> [1] 0.08983156 0.15380533 0.33759110 0.54674379 0.33170610 0.28865037 0.28250955
#> [8] 0.39500298 0.41466648
#> 
#> [[1]][[6]]
#> [1] 0.05182149 0.41743984 0.63520907 0.23623211 0.16353036 0.13451805 0.17102474
#> [8] 0.18293842 0.17436122
#> 
#> [[1]][[7]]
#> [1] 2.884425 2.994256 2.563328 2.528139 2.679789 2.455888 2.574421 3.270582
#> [9] 4.062107
#> 
#> [[1]][[8]]
#> [1] 1.5314888 0.9073527 0.8546632 0.9492293 1.0062567 1.2156124 1.2027054
#> [8] 1.2330194 1.3702081
#> 
#> [[1]][[9]]
#> [1] 0.7060897 0.5736736 0.9937846 0.6851302 0.6394279 0.5405938 0.5467194
#> [8] 0.5027723 0.4319827
#> 
#> [[1]][[10]]
#> [1] 1.4192751 1.0023379 0.7331192 0.1370833 0.1509492 0.1268596 0.2293369
#> [8] 0.2057744 0.1809544
#> 
#> 
bbbNA2 <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=4,modele="pls-glm-gaussian",verbose=FALSE)
bbbNA3 <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls-glm-gaussian",
K=10,verbose=FALSE)
kfolds2Chisqind(bbbNA2)
#> [[1]]
#> [[1]][[1]]
#> [1] 3.732495 2.225024 4.447632 4.114480
#> 
#> [[1]][[2]]
#> [1] 0.6279873 2.7322923 2.7248813 3.0625423
#> 
#> [[1]][[3]]
#> [1] 2.219986 2.908948 2.506402 3.155018
#> 
#> [[1]][[4]]
#> [1] 3.332160 2.288697 2.284680 2.121396
#> 
#> [[1]][[5]]
#> [1] 3.494277 4.032008 4.355750 4.206821
#> 
#> 
kfolds2Chisqind(bbbNA3)
#> [[1]]
#> [[1]][[1]]
#> [1] 0.6285543 1.0359499 1.2016547 1.7101698 2.0075539 2.1315108 2.2711194
#> [8] 2.2686850 1.9936127
#> 
#> [[1]][[2]]
#> [1] 1.6466467 1.8394761 0.7988124 0.4917480 0.4274220 9.0790939 8.6786108
#> [8] 8.6281858 8.5785090
#> 
#> [[1]][[3]]
#> [1] 1.268628 1.868241 1.797348 1.857962 2.079682 1.700354 1.903973 1.969352
#> [9] 1.628049
#> 
#> [[1]][[4]]
#> [1] 1.7816054 1.2688601 0.7779507 0.7852721 0.8003833 0.7908417 0.8954716
#> [8] 0.9792868 1.1159796
#> 
#> [[1]][[5]]
#> [1] 0.08856974 0.59773655 0.75148812 0.91397744 0.74650732 0.84620523 0.79797264
#> [8] 0.82348380 0.90358005
#> 
#> [[1]][[6]]
#> [1] 2.594153 1.505348 1.581181 1.384713 1.516205 1.523311 1.478979 1.151849
#> [9] 1.434312
#> 
#> [[1]][[7]]
#> [1] 1.4230173 1.0280495 0.8546950 0.9966400 0.8458524 0.9260959 1.0445783
#> [8] 1.0080508 0.9626158
#> 
#> [[1]][[8]]
#> [1] 0.7196986 0.8330835 0.5696433 0.6222657 0.6281209 0.7400389 0.7019334
#> [8] 0.6634473 0.6800500
#> 
#> [[1]][[9]]
#> [1] 3.171786 2.817886 2.889183 2.922070 2.798209 2.776305 2.655638 2.656450
#> [9] 2.511200
#> 
#> [[1]][[10]]
#> [1] 0.07212698 0.07553635 0.04590462 0.09351758 0.14290029 0.20322680 0.06330675
#> [8] 0.09910805 0.39491036
#> 
#> 
rm(list=c("Xpine","XpineNAX21","ypine","bbb","bbb2","bbbNA","bbbNA2","bbbNA3"))


data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-family",
family=binomial(),verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#> [1] 33.62170 50.17175 49.50281 76.64862
#> 
#> [[1]][[2]]
#> [1]  34.26170  37.72083  93.10709 159.96046
#> 
#> [[1]][[3]]
#> [1]  40.73370  96.09459 158.82797 182.84358
#> 
#> [[1]][[4]]
#> [1]  34.29291  50.56979 130.83437 319.72521
#> 
#> [[1]][[5]]
#> [1]  52.41381 122.81569 570.48530 754.27901
#> 
#> 
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic",
verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#> [1]    53.77817   176.70626  7126.73462 92631.85247
#> 
#> [[1]][[2]]
#> [1] 25.75493 20.38903 24.73161 30.68989
#> 
#> [[1]][[3]]
#> [1]  34.74578  52.02487 115.20079 358.91150
#> 
#> [[1]][[4]]
#> [1]  47.88129  56.84890 174.53571 474.03713
#> 
#> [[1]][[5]]
#> [1]   34.89712  109.58860 1541.95658 6085.02253
#> 
#> 
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-family",
family=binomial(),K=10,verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#>  [1] 20.17185 22.16585 40.55295 55.93092 54.54151 72.79754 77.48710 74.89419
#>  [9] 71.56772 68.86677
#> 
#> [[1]][[2]]
#>  [1]   22.67109   63.36670  140.63351  407.32449  850.17374 1763.66130
#>  [7] 1491.13289 1415.32001 1341.60415 1218.68858
#> 
#> [[1]][[3]]
#>  [1] 14.53021 23.92954 16.45789 20.11521 23.08082 38.61221 48.36176 75.60908
#>  [9] 79.85560 88.32230
#> 
#> [[1]][[4]]
#>  [1] 19.22960 16.80009 26.21448 53.15827 57.22353 60.53670 56.04207 54.29641
#>  [9] 53.26103 49.45670
#> 
#> [[1]][[5]]
#>  [1] 20.46798 29.02216 53.71628 79.91178 71.23519 64.77629 79.27302 80.07585
#>  [9] 78.21311 80.55105
#> 
#> [[1]][[6]]
#>  [1]  13.77671  14.12360  15.88668  51.92269  65.22225  70.83354 101.33980
#>  [8] 107.65828 103.01646 102.77375
#> 
#> [[1]][[7]]
#>  [1]  10.27710  20.42570  51.28554 213.80171 466.35445 628.88675 685.64130
#>  [8] 717.75229 692.34911 684.34020
#> 
#> [[1]][[8]]
#>  [1]  20.76713  22.56457  39.24716  63.70568 118.14793 118.55758 125.78246
#>  [8] 139.23459 134.37356 123.32337
#> 
#> [[1]][[9]]
#>  [1]  12.40417  20.23049  19.10320  25.47778  36.91847  89.08731 151.87048
#>  [8] 235.57441 259.93882 220.58283
#> 
#> [[1]][[10]]
#>  [1]   25.99583  102.20978  570.05803 1021.66235 1009.08935 1376.03875
#>  [7] 2449.94320 2387.73511 3028.54634 4416.69592
#> 
#> 
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,
modele="pls-glm-logistic",K=10,verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#>  [1]     26.49565     93.21469    921.38767   9538.76608  23909.87057
#>  [6]  49951.48671  78284.51590  84707.34522 102534.53032 135658.74676
#> 
#> [[1]][[2]]
#>  [1]  9.019219 11.398199 14.738368 16.875215 19.661211 16.615480 14.951250
#>  [8] 15.626287 15.474234 14.869145
#> 
#> [[1]][[3]]
#>  [1]  20.47939  23.61137  42.06091  53.25822 123.42004 165.70743 217.83578
#>  [8] 269.69151 274.99737 246.00232
#> 
#> [[1]][[4]]
#>  [1]   28.68566   41.83677  131.73007  412.62211  930.75241 1739.73520
#>  [7] 2990.78523 3621.11393 4857.33684 5536.99949
#> 
#> [[1]][[5]]
#>  [1] 19.31545 20.85890 18.27761 23.56477 17.40385 16.15048 15.15009 15.77489
#>  [9] 15.57694 14.57309
#> 
#> [[1]][[6]]
#>  [1] 16.21013 26.87634 20.11429 30.78639 48.05624 73.68477 83.49269 92.19472
#>  [9] 89.06331 83.31150
#> 
#> [[1]][[7]]
#>  [1] 31.69816 34.40746 40.30627 83.22161 87.01635 93.99141 96.40863 90.66529
#>  [9] 80.96131 81.01321
#> 
#> [[1]][[8]]
#>  [1]   7.700161  12.716496  29.081724  52.940247 128.007194 288.043362
#>  [7] 419.605015 434.864044 395.217274 377.389101
#> 
#> [[1]][[9]]
#>  [1]   40.5861  116.3598  737.6036 1073.7390 1388.0294 2342.8615 3098.8311
#>  [8] 3474.7578 2529.6038 1986.5046
#> 
#> [[1]][[10]]
#>  [1]  9.339474 14.378124 20.999670 26.588684 30.092950 32.874333 33.709496
#>  [8] 43.733913 40.296438 40.342693
#> 
#> 
rm(list=c("Xaze_compl","yaze_compl"))
# }