Light version of PLS_glm for cross validation purposes either on
complete or incomplete datasets.
Usage
PLS_glm_wvc(
dataY,
dataX,
nt = 2,
dataPredictY = dataX,
modele = "pls",
family = NULL,
scaleX = TRUE,
scaleY = NULL,
keepcoeffs = FALSE,
keepstd.coeffs = FALSE,
tol_Xi = 10^(-12),
weights,
method = "logistic",
verbose = TRUE
)Arguments
- dataY
response (training) dataset
- dataX
predictor(s) (training) dataset
- nt
number of components to be extracted
- dataPredictY
predictor(s) (testing) dataset
- modele
name of the PLS glm model to be fitted (
"pls","pls-glm-Gamma","pls-glm-gaussian","pls-glm-inverse.gaussian","pls-glm-logistic","pls-glm-poisson","pls-glm-polr"). Use"modele=pls-glm-family"to enable thefamilyoption.- family
a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See
familyfor details of family functions.) To use the family option, please setmodele="pls-glm-family". User defined families can also be defined. See details.- scaleX
scale the predictor(s) : must be set to TRUE for
modele="pls"and should be for glms pls.- scaleY
scale the response : Yes/No. Ignored since non always possible for glm responses.
- keepcoeffs
whether the coefficients of the linear fit on link scale of unstandardized eXplanatory variables should be returned or not.
- keepstd.coeffs
whether the coefficients of the linear fit on link scale of standardized eXplanatory variables should be returned or not.
- tol_Xi
minimal value for Norm2(Xi) and \(\mathrm{det}(pp' \times pp)\) if there is any missing value in the
dataX. It defaults to \(10^{-12}\)- weights
an optional vector of 'prior weights' to be used in the fitting process. Should be
NULLor a numeric vector.- method
logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
- verbose
should info messages be displayed ?
Value
- valsPredict
nrow(dataPredictY) * ntmatrix of the predicted values- list("coeffs")
If the coefficients of the eXplanatory variables were requested:
i.e.keepcoeffs=TRUE.ncol(dataX) * 1matrix of the coefficients of the the eXplanatory variables
Details
This function is called by PLS_glm_kfoldcv_formula in order to
perform cross-validation either on complete or incomplete datasets.
There are seven different predefined models with predefined link functions available :
- list("\"pls\"")
ordinary pls models
- list("\"pls-glm-Gamma\"")
glm gaussian with inverse link pls models
- list("\"pls-glm-gaussian\"")
glm gaussian with identity link pls models
- list("\"pls-glm-inverse-gamma\"")
glm binomial with square inverse link pls models
- list("\"pls-glm-logistic\"")
glm binomial with logit link pls models
- list("\"pls-glm-poisson\"")
glm poisson with log link pls models
- list("\"pls-glm-polr\"")
glm polr with logit link pls models
Using the "family=" option and setting
"modele=pls-glm-family" allows changing the family and link function
the same way as for the glm function. As a consequence
user-specified families can also be used.
- The
accepts the links (as names)
identity,logandinverse.- list("gaussian")
accepts the links (as names)
identity,logandinverse.- family
accepts the links (as names)
identity,logandinverse.- The
accepts the links
logit,probit,cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively)logandcloglog(complementary log-log).- list("binomial")
accepts the links
logit,probit,cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively)logandcloglog(complementary log-log).- family
accepts the links
logit,probit,cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively)logandcloglog(complementary log-log).- The
accepts the links
inverse,identityandlog.- list("Gamma")
accepts the links
inverse,identityandlog.- family
accepts the links
inverse,identityandlog.- The
accepts the links
log,identity, andsqrt.- list("poisson")
accepts the links
log,identity, andsqrt.- family
accepts the links
log,identity, andsqrt.- The
accepts the links
1/mu^2,inverse,identityandlog.- list("inverse.gaussian")
accepts the links
1/mu^2,inverse,identityandlog.- family
accepts the links
1/mu^2,inverse,identityandlog.- The
accepts the links
logit,probit,cloglog,identity,inverse,log,1/mu^2andsqrt.- list("quasi")
accepts the links
logit,probit,cloglog,identity,inverse,log,1/mu^2andsqrt.- family
accepts the links
logit,probit,cloglog,identity,inverse,log,1/mu^2andsqrt.- The function
can be used to create a power link function.
- list("power")
can be used to create a power link function.
Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.
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
PLS_glm for more detailed results,
PLS_glm_kfoldcv for cross-validating models and
PLS_lm_wvc for the same function dedicated to plsR models
Author
Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/
Examples
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_glm_wvc(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,])
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> $valsPredict
#> [,1] [,2] [,3]
#> 1 95.03164 97.08409 97.4436
#>
PLS_glm_wvc(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-family",
family=gaussian(),dataPredictY=XCornell[1,], verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3]
#> 1 95.03164 97.08409 97.4436
#>
PLS_glm_wvc(dataY=yCornell[-1],dataX=XCornell[-1,],nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,], verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3]
#> 1 93.74777 95.32475 96.08522
#>
PLS_glm_wvc(dataY=yCornell[-1],dataX=XCornell[-1,],nt=3,modele="pls-glm-family",
family=gaussian(),dataPredictY=XCornell[1,], verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3]
#> 1 93.74777 95.32475 96.08522
#>
rm("XCornell","yCornell")
# \donttest{
## With an incomplete dataset (X[1,2] is NA)
data(pine)
ypine <- pine[,11]
data(XpineNAX21)
PLS_glm_wvc(dataY=ypine,dataX=XpineNAX21,nt=10,modele="pls-glm-gaussian")
#> ____************************************************____
#> Only naive DoF can be used with missing data
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____There are some NAs in X but not in Y____
#> ____Predicting X with NA in X and not in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ****________________________________________________****
#>
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 1.4539431721 2.58686302 2.6090759 2.65655978 3.15883072 3.42926182
#> 2 0.9803000295 1.16058533 1.1833005 1.28488164 1.18517618 1.17351469
#> 3 1.5421433729 1.20822242 1.3853553 1.15999892 1.06119789 0.99352272
#> 4 0.8634757400 0.61153667 0.9598232 0.63676710 0.73217251 0.72439005
#> 5 1.1046967056 0.72056610 0.5615100 0.48002572 0.05031434 -0.02890243
#> 6 1.3430089378 1.44124063 1.2350616 1.35213181 1.07499774 1.00153522
#> 7 0.4653011509 -0.55778608 -0.1345810 -0.40312929 -0.11007833 0.02486483
#> 8 0.7245706631 0.77453614 -0.1329786 0.06547790 0.31942685 -0.06589345
#> 9 1.4768276059 1.51567612 1.6250574 1.59073347 1.97691808 2.06744335
#> 10 1.0240822662 0.87210913 0.6081081 0.51995031 1.25860279 1.17823388
#> 11 0.3376379927 1.17893648 0.7989349 0.59109134 0.67492159 0.66971895
#> 12 -0.1361926285 0.32400051 0.1088808 0.02503985 1.09413721 0.91614621
#> 13 1.7251506342 1.94257097 2.3721225 2.30673885 2.08307096 2.12519360
#> 14 1.2983306330 1.75308300 1.6929078 1.99562658 1.58229858 1.60648610
#> 15 1.2673647925 1.36309370 1.3509504 1.50028142 1.56068200 1.66788433
#> 16 -0.2742212930 -0.54947877 -0.4625418 -0.45217350 -0.43900249 -0.54284805
#> 17 0.0726132592 0.09110250 0.2767850 0.38572317 -0.46094657 -0.37755959
#> 18 1.0119232701 1.24823667 1.0358333 0.93127818 1.04973056 1.07268197
#> 19 1.1712169069 0.45709538 0.7154129 0.69975186 1.04627874 1.12890339
#> 20 0.3380528810 -0.05282248 -0.1359717 -0.17511644 0.01795423 -0.03635962
#> 21 -0.0006184277 0.21271797 1.0933839 1.10740476 0.60193100 0.61977791
#> 22 0.6475528007 0.64676752 0.9677756 0.88988795 0.74500454 0.78221342
#> 23 0.9069905284 0.88140911 0.7681153 1.08147288 0.95938321 1.05823268
#> 24 0.2908076924 0.12961264 0.8282876 0.95768372 0.56951175 0.59933236
#> 25 0.6399372516 0.14296644 -0.4236403 -0.37407716 -0.44329114 -0.69039703
#> 26 0.9183707395 0.90065654 0.8887530 1.09037413 0.98475758 1.18762561
#> 27 -0.0660539634 0.61584030 0.2968035 0.37286685 -0.40233133 -0.23591051
#> 28 1.6294091667 1.01981261 1.4487065 1.30885327 1.04296746 1.02750273
#> 29 1.4527596503 1.39098952 1.6208012 1.60355751 1.81341376 1.91094735
#> 30 0.7455927900 0.74230137 0.7595845 0.45727580 0.79191445 0.79398217
#> 31 0.9251914554 1.22235139 0.9675384 1.27926735 1.60673050 1.62225025
#> 32 0.6030109074 1.10766233 0.9423622 0.97635769 0.97873680 1.07730348
#> 33 0.2463233753 0.05289306 -0.1983792 -0.15328541 0.20920986 0.37188736
#> [,7] [,8] [,9]
#> 1 3.41392627 2.96083291 3.23542261
#> 2 1.09536968 0.93205421 0.88650726
#> 3 1.29732525 1.20790868 1.22562598
#> 4 0.93697105 0.96071216 1.00900607
#> 5 0.34395975 0.31660424 0.35664778
#> 6 1.25769379 1.27286284 1.31025110
#> 7 0.10061500 0.20716458 0.22337146
#> 8 -0.06749700 -0.45235173 -0.53735541
#> 9 1.84752454 1.96619927 1.95995370
#> 10 1.07258576 1.25598280 1.30528130
#> 11 0.65182003 0.67547129 0.66502359
#> 12 0.82760907 0.63803800 0.59597012
#> 13 2.25015844 2.33499027 2.34358840
#> 14 1.83700630 1.92773146 2.00021878
#> 15 1.64781018 1.69047876 1.67309741
#> 16 -0.53535935 -0.55349482 -0.58253557
#> 17 -0.09360753 -0.07937692 0.06042429
#> 18 0.73514311 0.39733701 0.23952431
#> 19 1.03072981 1.07397224 1.07267962
#> 20 -0.08754010 -0.20699608 -0.25853347
#> 21 0.58792158 0.61901916 0.60223241
#> 22 0.72354893 0.65192523 0.61620016
#> 23 0.86644827 0.94744835 0.95335224
#> 24 0.38330515 0.46140202 0.44202144
#> 25 -0.43279587 -0.59408406 -0.57013118
#> 26 0.93104122 0.98212754 0.92705304
#> 27 0.20209342 0.32284534 0.38231639
#> 28 1.01171393 1.02783639 0.97721921
#> 29 1.78120212 1.87739994 1.82673047
#> 30 0.47349187 0.52149603 0.52673297
#> 31 1.46531670 1.50285087 1.51032980
#> 32 0.87652870 1.16647296 1.16995441
#> 33 0.28101723 0.47212049 0.52841275
#>
rm("XpineNAX21","ypine")
#> Warning: object 'XpineNAX21' not found
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
PLS_glm_wvc(ypine,Xpine,10,modele="pls", verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 1.57673729 2.00433040 2.00216849 2.01939633 1.937245401 1.88550891
#> 2 1.01203030 1.15199511 1.11571861 1.24570213 1.235802532 1.20179188
#> 3 1.48993594 1.24149458 1.36818432 1.53585542 1.486704711 1.51671911
#> 4 0.82287026 0.63250520 0.93956109 1.04161870 1.007232341 1.04125064
#> 5 1.03843648 0.73560935 0.56129947 0.58902449 0.528441708 0.57134580
#> 6 1.35675180 1.40730081 1.25451432 1.37731103 1.366862539 1.39997555
#> 7 0.32014006 -0.32532009 -0.14347163 -0.17938500 -0.165426534 -0.11535438
#> 8 0.71839021 0.73099520 0.30866945 0.39516648 0.412751496 0.32418945
#> 9 1.48896024 1.52799376 1.67587131 1.60139990 1.652717167 1.65007715
#> 10 0.99804354 0.88959103 0.97569244 0.86352520 0.947912813 0.94847153
#> 11 0.43405362 0.86608054 0.97012047 0.64182858 0.616856901 0.63882421
#> 12 -0.07021385 0.26999887 0.59312200 0.92157715 1.008253831 0.98380367
#> 13 1.76424139 1.90090137 2.11336407 2.19169300 2.166416346 2.19486369
#> 14 1.37638458 1.70611149 1.58504677 1.84688715 1.857927378 1.90455582
#> 15 1.29114690 1.38753932 1.35365960 1.43462512 1.476219895 1.51027552
#> 16 -0.31402446 -0.47052602 -0.50369169 -0.48552205 -0.489472457 -0.51351160
#> 17 0.07868727 0.09889902 -0.01564596 0.15763382 0.083278591 0.10574381
#> 18 1.03444868 1.14007872 1.09595610 0.88458167 0.852892800 0.78377367
#> 19 1.08137124 0.67811651 0.73915351 0.82968208 0.879143668 0.88040097
#> 20 0.27838475 0.03080907 -0.03681897 -0.06160100 -0.054332827 -0.07763891
#> 21 0.04732997 0.24640318 0.51964685 0.71563687 0.653313657 0.61594736
#> 22 0.64993047 0.65252960 0.76864397 0.76705111 0.732874483 0.71165796
#> 23 0.91576469 0.95152885 0.70877390 0.68206508 0.726537887 0.71111241
#> 24 0.28714240 0.25747771 0.32732647 0.36607751 0.340816493 0.27266379
#> 25 0.55621083 0.20897499 -0.13621852 -0.01161858 -0.029168653 -0.04249364
#> 26 0.92489518 0.94649310 0.77909889 0.64060202 0.670826485 0.67364464
#> 27 0.01702572 0.38181268 0.20327363 0.07976920 0.023726459 0.15754257
#> 28 1.54352449 1.13003525 1.14773845 0.98446655 0.935861794 0.90956750
#> 29 1.45181367 1.42783969 1.54487387 1.46033916 1.495705868 1.51049933
#> 30 0.73123225 0.66412607 0.84569291 0.49408354 0.482259156 0.42326984
#> 31 0.98197420 1.24443724 1.14351551 1.31718138 1.406786445 1.39942166
#> 32 0.66850881 0.94811355 0.92220097 0.50456716 0.531183973 0.55048739
#> 33 0.21787107 0.10572384 0.04295933 -0.08122119 -0.008152348 0.04161271
#> [,7] [,8] [,9] [,10]
#> 1 1.95676227 1.948450849 1.93896093 1.94144962
#> 2 1.21592652 1.235442425 1.24794773 1.24303207
#> 3 1.54977163 1.524520187 1.53266664 1.53900851
#> 4 1.05581321 1.021862826 1.03178760 1.03548430
#> 5 0.58624206 0.562391104 0.56933856 0.58993331
#> 6 1.38175170 1.360508864 1.35664763 1.36575593
#> 7 -0.07128741 -0.047428997 -0.05482975 -0.06507565
#> 8 0.35423193 0.306999026 0.28300642 0.26870841
#> 9 1.63319069 1.631509959 1.64240206 1.64555642
#> 10 0.90453538 0.837015730 0.82600711 0.82493397
#> 11 0.66440603 0.619480777 0.61806414 0.61605314
#> 12 1.06477362 1.031255436 1.02700955 1.02592619
#> 13 2.16399005 2.163911302 2.15049944 2.14429918
#> 14 1.84909183 1.845501860 1.84534215 1.84361941
#> 15 1.53220512 1.567310225 1.56848600 1.56902383
#> 16 -0.51165239 -0.500336508 -0.52095379 -0.50662867
#> 17 0.01828375 -0.002607768 0.01667444 -0.02206645
#> 18 0.88281941 0.918670514 0.94195909 0.93284893
#> 19 0.88632519 0.909007733 0.91379654 0.90631555
#> 20 -0.03748213 -0.025963454 -0.03697219 -0.04597628
#> 21 0.56329746 0.606867697 0.60725951 0.63204655
#> 22 0.71534817 0.734795585 0.73328612 0.71985709
#> 23 0.66101344 0.690882417 0.69856666 0.69360847
#> 24 0.17883038 0.221072111 0.21360194 0.22005958
#> 25 -0.02445300 -0.070467998 -0.05897223 -0.03210573
#> 26 0.69268993 0.763387338 0.78043784 0.79028544
#> 27 0.19700010 0.215177839 0.20405047 0.19800511
#> 28 0.87932761 0.893834308 0.86843397 0.85997782
#> 29 1.52036274 1.552065698 1.53445170 1.53023569
#> 30 0.38164488 0.326448839 0.35187384 0.34979385
#> 31 1.37872533 1.385981563 1.39371317 1.39564352
#> 32 0.49904917 0.493233383 0.48196396 0.49873722
#> 33 0.04746533 0.049219132 0.06349274 0.06165365
#>
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-Gamma", verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> 1 1.7021625 3.3482083 2.7139565 2.4058093 3.2467830 2.2037424 2.5521714
#> 2 0.6639038 0.6387631 0.6121317 0.7600887 0.9482646 0.9294531 0.8653260
#> 3 1.3088626 0.8281449 0.8781982 0.8175537 1.0345037 1.1660944 1.3209167
#> 4 0.5006322 0.4014678 0.4796826 0.4658354 0.4844424 0.4841038 0.5449712
#> 5 0.6854677 0.5251666 0.4284003 0.3615301 0.3586455 0.3703400 0.3739103
#> 6 1.1713845 1.2407194 0.9038012 0.8025733 0.7106149 0.7508921 0.7329991
#> 7 0.3549836 0.2691848 0.2777883 0.2608888 0.2663969 0.2714017 0.2817687
#> 8 0.5120913 0.5899263 0.5143725 0.4952195 0.4492835 0.4160523 0.3486881
#> 9 1.4945619 1.8984358 3.2406348 2.6184777 2.9855578 3.5895484 2.8384646
#> 10 0.6500508 0.7579531 1.0736393 0.7529701 0.5609868 0.5445539 0.4945195
#> 11 0.3849210 0.4622240 0.4980263 0.3569521 0.3024332 0.2995254 0.3421787
#> 12 0.2795105 0.3152286 0.4814740 0.6678051 0.6433927 0.5911492 0.7463694
#> 13 4.2141188 3.0076725 3.4791475 4.2092704 3.2534025 2.9808121 3.3581408
#> 14 1.2733917 1.8325125 1.2115241 1.6556086 1.3004955 1.5582862 1.6386778
#> 15 1.0345150 1.2033342 1.0290148 1.0809521 1.1747941 1.4991592 1.7732216
#> 16 0.2576160 0.2298102 0.2306029 0.2393888 0.2219856 0.2038692 0.1991727
#> 17 0.3181072 0.2641593 0.2394775 0.2465446 0.2434009 0.2454800 0.2454886
#> 18 0.6689827 0.6826523 0.6317041 0.5589350 0.7682575 0.8003709 0.8141919
#> 19 0.7212759 0.5417550 0.5624130 0.6169171 0.7357709 0.7839123 0.6859125
#> 20 0.3544669 0.3125267 0.3071243 0.3004883 0.2983363 0.2888686 0.2809863
#> 21 0.3010344 0.2531795 0.2689006 0.3545433 0.3516489 0.2928523 0.2888406
#> 22 0.4524566 0.3845643 0.3876906 0.4082754 0.4216088 0.3971631 0.3949778
#> 23 0.6507469 0.6608146 0.5157721 0.5355239 0.5223465 0.5289271 0.4564660
#> 24 0.3581027 0.2902701 0.2841434 0.3507472 0.3384621 0.2851532 0.2518528
#> 25 0.4407103 0.3888439 0.3464864 0.3246940 0.3226049 0.3196535 0.2975328
#> 26 0.6435771 0.6370758 0.4976960 0.4812621 0.5249918 0.5591198 0.5489260
#> 27 0.3096927 0.3200657 0.2664525 0.2208262 0.1962616 0.2068674 0.2457191
#> 28 1.7027982 0.7863943 0.6191251 0.5376817 0.5259622 0.4714803 0.3925142
#> 29 1.3624280 1.3395903 1.3437875 1.1678080 1.0829979 1.0899791 1.0607518
#> 30 0.4809691 0.4482798 0.5264782 0.4211320 0.4080366 0.3796394 0.3529260
#> 31 0.6795166 0.9667644 1.0310662 1.5914495 1.4876477 1.6576885 1.4173828
#> 32 0.4867971 0.5929337 0.5403197 0.3962608 0.3103903 0.2955165 0.2975775
#> 33 0.3501638 0.3513783 0.3489678 0.3059872 0.2892922 0.3083446 0.3264570
#> [,8] [,9] [,10]
#> 1 2.4848460 2.5383659 2.5386650
#> 2 0.8818512 0.8504103 0.8504414
#> 3 1.3240837 1.3853373 1.3858636
#> 4 0.5790253 0.5768843 0.5769496
#> 5 0.3921145 0.4055536 0.4055933
#> 6 0.7330597 0.7621053 0.7622316
#> 7 0.2660918 0.2660232 0.2660038
#> 8 0.2912579 0.2917354 0.2917732
#> 9 3.2596209 3.2484587 3.2483633
#> 10 0.4443817 0.4485445 0.4486040
#> 11 0.3542984 0.3444401 0.3444444
#> 12 0.6853879 0.6926198 0.6926340
#> 13 3.2365795 3.1305159 3.1301005
#> 14 1.7486322 1.7094242 1.7099539
#> 15 1.6651777 1.7173913 1.7167684
#> 16 0.2027942 0.2069252 0.2069125
#> 17 0.2423289 0.2281793 0.2282071
#> 18 0.7941254 0.7399260 0.7398598
#> 19 0.6071330 0.6085899 0.6085694
#> 20 0.2637502 0.2634246 0.2634166
#> 21 0.3540481 0.3637124 0.3636558
#> 22 0.3923977 0.3816768 0.3816594
#> 23 0.4520624 0.4454465 0.4454376
#> 24 0.2724808 0.2743025 0.2742785
#> 25 0.3016025 0.3133463 0.3133922
#> 26 0.6115410 0.6177281 0.6175658
#> 27 0.2518436 0.2473006 0.2472828
#> 28 0.3629759 0.3659599 0.3659410
#> 29 0.9255826 0.9503453 0.9500157
#> 30 0.3723393 0.3588267 0.3588724
#> 31 1.3702410 1.3900717 1.3901525
#> 32 0.3220191 0.3261020 0.3260694
#> 33 0.3243259 0.3203266 0.3203212
#>
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-family",family=Gamma(), verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> 1 1.7021625 3.3482083 2.7139565 2.4058093 3.2467830 2.2037424 2.5521714
#> 2 0.6639038 0.6387631 0.6121317 0.7600887 0.9482646 0.9294531 0.8653260
#> 3 1.3088626 0.8281449 0.8781982 0.8175537 1.0345037 1.1660944 1.3209167
#> 4 0.5006322 0.4014678 0.4796826 0.4658354 0.4844424 0.4841038 0.5449712
#> 5 0.6854677 0.5251666 0.4284003 0.3615301 0.3586455 0.3703400 0.3739103
#> 6 1.1713845 1.2407194 0.9038012 0.8025733 0.7106149 0.7508921 0.7329991
#> 7 0.3549836 0.2691848 0.2777883 0.2608888 0.2663969 0.2714017 0.2817687
#> 8 0.5120913 0.5899263 0.5143725 0.4952195 0.4492835 0.4160523 0.3486881
#> 9 1.4945619 1.8984358 3.2406348 2.6184777 2.9855578 3.5895484 2.8384646
#> 10 0.6500508 0.7579531 1.0736393 0.7529701 0.5609868 0.5445539 0.4945195
#> 11 0.3849210 0.4622240 0.4980263 0.3569521 0.3024332 0.2995254 0.3421787
#> 12 0.2795105 0.3152286 0.4814740 0.6678051 0.6433927 0.5911492 0.7463694
#> 13 4.2141188 3.0076725 3.4791475 4.2092704 3.2534025 2.9808121 3.3581408
#> 14 1.2733917 1.8325125 1.2115241 1.6556086 1.3004955 1.5582862 1.6386778
#> 15 1.0345150 1.2033342 1.0290148 1.0809521 1.1747941 1.4991592 1.7732216
#> 16 0.2576160 0.2298102 0.2306029 0.2393888 0.2219856 0.2038692 0.1991727
#> 17 0.3181072 0.2641593 0.2394775 0.2465446 0.2434009 0.2454800 0.2454886
#> 18 0.6689827 0.6826523 0.6317041 0.5589350 0.7682575 0.8003709 0.8141919
#> 19 0.7212759 0.5417550 0.5624130 0.6169171 0.7357709 0.7839123 0.6859125
#> 20 0.3544669 0.3125267 0.3071243 0.3004883 0.2983363 0.2888686 0.2809863
#> 21 0.3010344 0.2531795 0.2689006 0.3545433 0.3516489 0.2928523 0.2888406
#> 22 0.4524566 0.3845643 0.3876906 0.4082754 0.4216088 0.3971631 0.3949778
#> 23 0.6507469 0.6608146 0.5157721 0.5355239 0.5223465 0.5289271 0.4564660
#> 24 0.3581027 0.2902701 0.2841434 0.3507472 0.3384621 0.2851532 0.2518528
#> 25 0.4407103 0.3888439 0.3464864 0.3246940 0.3226049 0.3196535 0.2975328
#> 26 0.6435771 0.6370758 0.4976960 0.4812621 0.5249918 0.5591198 0.5489260
#> 27 0.3096927 0.3200657 0.2664525 0.2208262 0.1962616 0.2068674 0.2457191
#> 28 1.7027982 0.7863943 0.6191251 0.5376817 0.5259622 0.4714803 0.3925142
#> 29 1.3624280 1.3395903 1.3437875 1.1678080 1.0829979 1.0899791 1.0607518
#> 30 0.4809691 0.4482798 0.5264782 0.4211320 0.4080366 0.3796394 0.3529260
#> 31 0.6795166 0.9667644 1.0310662 1.5914495 1.4876477 1.6576885 1.4173828
#> 32 0.4867971 0.5929337 0.5403197 0.3962608 0.3103903 0.2955165 0.2975775
#> 33 0.3501638 0.3513783 0.3489678 0.3059872 0.2892922 0.3083446 0.3264570
#> [,8] [,9] [,10]
#> 1 2.4848460 2.5383659 2.5386650
#> 2 0.8818512 0.8504103 0.8504414
#> 3 1.3240837 1.3853373 1.3858636
#> 4 0.5790253 0.5768843 0.5769496
#> 5 0.3921145 0.4055536 0.4055933
#> 6 0.7330597 0.7621053 0.7622316
#> 7 0.2660918 0.2660232 0.2660038
#> 8 0.2912579 0.2917354 0.2917732
#> 9 3.2596209 3.2484587 3.2483633
#> 10 0.4443817 0.4485445 0.4486040
#> 11 0.3542984 0.3444401 0.3444444
#> 12 0.6853879 0.6926198 0.6926340
#> 13 3.2365795 3.1305159 3.1301005
#> 14 1.7486322 1.7094242 1.7099539
#> 15 1.6651777 1.7173913 1.7167684
#> 16 0.2027942 0.2069252 0.2069125
#> 17 0.2423289 0.2281793 0.2282071
#> 18 0.7941254 0.7399260 0.7398598
#> 19 0.6071330 0.6085899 0.6085694
#> 20 0.2637502 0.2634246 0.2634166
#> 21 0.3540481 0.3637124 0.3636558
#> 22 0.3923977 0.3816768 0.3816594
#> 23 0.4520624 0.4454465 0.4454376
#> 24 0.2724808 0.2743025 0.2742785
#> 25 0.3016025 0.3133463 0.3133922
#> 26 0.6115410 0.6177281 0.6175658
#> 27 0.2518436 0.2473006 0.2472828
#> 28 0.3629759 0.3659599 0.3659410
#> 29 0.9255826 0.9503453 0.9500157
#> 30 0.3723393 0.3588267 0.3588724
#> 31 1.3702410 1.3900717 1.3901525
#> 32 0.3220191 0.3261020 0.3260694
#> 33 0.3243259 0.3203266 0.3203212
#>
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-gaussian", verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 1.57673729 2.112703071 2.04925275 2.025720690 1.99988877 1.9833376732
#> 2 1.01203030 1.097055838 1.16768196 1.188171412 1.15718087 1.2424038010
#> 3 1.48993594 1.408750619 1.48576816 1.417980819 1.47582762 1.4982947689
#> 4 0.82287026 0.920756079 1.01128635 0.901257566 0.97491134 0.9909505955
#> 5 1.03843648 0.659104154 0.56302369 0.539466433 0.54522439 0.5188201727
#> 6 1.35675180 1.363858458 1.34528931 1.394223504 1.37942281 1.3306250748
#> 7 0.32014006 -0.208936487 -0.12375412 -0.181775915 -0.06686014 -0.0207190905
#> 8 0.71839021 0.466159761 0.40368534 0.513587769 0.48884774 0.3578640505
#> 9 1.48896024 1.577050840 1.58976688 1.576666292 1.60118999 1.6121834444
#> 10 0.99804354 0.955521315 0.91591324 0.915171230 0.97502649 0.8174125637
#> 11 0.43405362 1.004865089 0.70022268 0.636399772 0.65960111 0.6245784031
#> 12 -0.07021385 0.625346725 0.85018705 0.906678742 1.04976341 1.0296261669
#> 13 1.76424139 2.130136647 2.23589140 2.197201851 2.19275527 2.1697925483
#> 14 1.37638458 1.681218703 1.75409814 1.849649772 1.80173880 1.8135307368
#> 15 1.29114690 1.361382091 1.39654708 1.475461977 1.50322338 1.5668160958
#> 16 -0.31402446 -0.467204941 -0.38855760 -0.373068343 -0.38143767 -0.4913222929
#> 17 0.07868727 -0.009485106 0.00923828 -0.036909766 -0.14037506 0.0003648119
#> 18 1.03444868 1.006290938 0.84699467 0.805974957 0.80434945 0.9670404542
#> 19 1.08137124 0.652213314 0.80163323 0.811023094 0.86280576 0.9155210110
#> 20 0.27838475 -0.032388253 -0.02071891 -0.009618538 0.02030676 0.0147427279
#> 21 0.04732997 0.463005953 0.75191452 0.690072944 0.59756638 0.5787557488
#> 22 0.64993047 0.723791521 0.78678336 0.734843252 0.71613035 0.7677772238
#> 23 0.91576469 0.661826559 0.63945101 0.723928013 0.65099726 0.6902564661
#> 24 0.28714240 0.226510895 0.44320269 0.409261632 0.27917608 0.2269039458
#> 25 0.55621083 -0.024808263 -0.08836434 -0.049135741 -0.04935391 -0.1262912840
#> 26 0.92489518 0.696833356 0.61254496 0.676658656 0.64557351 0.7620516870
#> 27 0.01702572 0.383663431 0.09067225 0.142983517 0.15295203 0.2265845367
#> 28 1.54352449 1.118282238 1.14733091 1.067690338 1.02839043 0.9429046207
#> 29 1.45181367 1.511879599 1.55176709 1.566714783 1.60155782 1.5828522058
#> 30 0.73123225 0.675786261 0.50840164 0.348494547 0.32640081 0.3072993447
#> 31 0.98197420 1.138509770 1.22086791 1.348938103 1.34491760 1.3622819484
#> 32 0.66850881 0.900496012 0.64433614 0.644006458 0.60182468 0.4779691553
#> 33 0.21787107 -0.010176189 -0.13235770 -0.087719821 -0.02952413 0.0287906836
#> [,7] [,8] [,9] [,10]
#> 1 1.95998953 1.94645747 1.93186966 1.94144962
#> 2 1.25598752 1.24287885 1.23986128 1.24303207
#> 3 1.49520811 1.51724573 1.53404270 1.53900851
#> 4 0.99891106 1.01599568 1.03395498 1.03548430
#> 5 0.51896770 0.56790205 0.58389496 0.58993331
#> 6 1.32539225 1.34603852 1.36392791 1.36575593
#> 7 -0.06196351 -0.05464413 -0.06141636 -0.06507565
#> 8 0.33392253 0.26216021 0.26205140 0.26870841
#> 9 1.65121630 1.65247099 1.64949308 1.64555642
#> 10 0.84217676 0.81747252 0.83103198 0.82493397
#> 11 0.62520462 0.61633899 0.61661614 0.61605314
#> 12 1.05350867 1.02479904 1.02287949 1.02592619
#> 13 2.13743805 2.13939290 2.14749253 2.14429918
#> 14 1.80845036 1.81864863 1.84537429 1.84361941
#> 15 1.56195461 1.57681740 1.57071745 1.56902383
#> 16 -0.50961890 -0.49310993 -0.50826612 -0.50662867
#> 17 -0.02051672 -0.06780741 -0.01988610 -0.02206645
#> 18 0.98706465 0.95905848 0.92650134 0.93284893
#> 19 0.91934368 0.91222031 0.90916925 0.90631555
#> 20 -0.01218066 -0.03394867 -0.04715924 -0.04597628
#> 21 0.59411953 0.64146282 0.62996595 0.63204655
#> 22 0.74682044 0.72671756 0.72012579 0.71985709
#> 23 0.70899785 0.69938422 0.69733168 0.69360847
#> 24 0.23235605 0.23628193 0.22277700 0.22005958
#> 25 -0.08891154 -0.05778064 -0.04168003 -0.03210573
#> 26 0.78675646 0.81693166 0.79133604 0.79028544
#> 27 0.15249578 0.18906869 0.19908660 0.19800511
#> 28 0.88885380 0.87522045 0.86246092 0.85997782
#> 29 1.55342327 1.55350046 1.53516894 1.53023569
#> 30 0.37656252 0.34741004 0.35160380 0.34979385
#> 31 1.40075691 1.39407660 1.39778420 1.39564352
#> 32 0.49263192 0.51865275 0.50530217 0.49873722
#> 33 0.05468043 0.06268582 0.06658628 0.06165365
#>
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-family",family=gaussian(log), verbose=FALSE)
#> Warning: glm.fit: algorithm did not converge
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> 1 1.94034398 2.71462895 2.43467867 2.34032266 2.49387242 2.52177239 2.36881223
#> 2 0.75808067 0.69121040 0.65620818 0.70721651 1.04029001 0.98040711 0.93783784
#> 3 1.40934523 1.05235219 1.16507753 1.04330692 1.27590261 1.35755324 1.37414207
#> 4 0.37708016 0.35352178 0.46140678 0.36128092 0.61457152 0.56530388 0.62061981
#> 5 0.77818425 0.53104971 0.43760160 0.37373842 0.34144739 0.34208810 0.33370653
#> 6 1.51169791 1.44153372 1.26663946 1.31087294 1.06609858 1.04718818 1.03518591
#> 7 0.16713747 0.08885717 0.09752329 0.06901046 0.10159776 0.10286735 0.11602626
#> 8 0.52169803 0.45181518 0.39494988 0.45271597 0.35170317 0.30990886 0.24700339
#> 9 1.72168458 2.00061811 2.45997527 2.60364609 2.49269049 2.67113548 2.60678716
#> 10 0.69017124 0.87663394 1.23358029 1.21064587 0.88829904 0.83085920 0.79099293
#> 11 0.24761936 0.59543246 0.61011139 0.43755305 0.41628614 0.43157686 0.53045450
#> 12 0.07788296 0.13938476 0.23445656 0.21678007 0.54126772 0.46650102 0.53587964
#> 13 2.54607327 2.73230801 2.90764248 2.80193225 2.88622102 2.71241728 2.82081773
#> 14 1.66719840 1.88972107 1.63632610 1.91063363 1.84158614 1.73408495 1.86193764
#> 15 1.34895761 1.33460645 1.23252238 1.29571895 1.25365525 1.45657367 1.58051773
#> 16 0.06341544 0.04781972 0.04523887 0.03588003 0.06230984 0.03910136 0.03595447
#> 17 0.13804789 0.10480764 0.08384257 0.07535406 0.14547767 0.10826649 0.12411130
#> 18 0.79061549 0.82574247 0.74064232 0.69586410 0.87182540 1.09564836 1.06117605
#> 19 0.78786507 0.45717855 0.51628995 0.52758141 0.65036236 0.66698463 0.63632866
#> 20 0.18952817 0.13436883 0.12758699 0.10907087 0.14319721 0.12855285 0.12477305
#> 21 0.10522301 0.09131829 0.09479376 0.08113521 0.28581761 0.13871715 0.12566135
#> 22 0.34302383 0.29785876 0.29338908 0.25447835 0.42009515 0.35272268 0.35674133
#> 23 0.81820041 0.66972983 0.54855397 0.64001770 0.59570640 0.55276326 0.50977543
#> 24 0.19474253 0.12654062 0.12041800 0.11763698 0.25419417 0.12872552 0.10208317
#> 25 0.35299833 0.22248594 0.19757934 0.19485657 0.19771813 0.17411768 0.14585414
#> 26 0.80124529 0.67773527 0.52264350 0.53603714 0.54379607 0.60854112 0.60682863
#> 27 0.14150887 0.22211358 0.13221850 0.08582109 0.08069217 0.09245398 0.14580144
#> 28 1.81316065 0.97154149 0.87534137 0.78129461 0.64910997 0.54803145 0.45120061
#> 29 1.61509512 1.55735811 1.60106153 1.52704385 1.31898041 1.40585691 1.42994630
#> 30 0.39043233 0.49977789 0.66789433 0.57577267 0.69466570 0.59985551 0.53879331
#> 31 0.83475962 0.99424242 1.05056360 1.31875471 1.42449237 1.39059830 1.37115108
#> 32 0.47025561 0.82350400 0.75180162 0.62855746 0.42921486 0.37592533 0.38664416
#> 33 0.19382221 0.20784671 0.20961391 0.18342774 0.18891735 0.21202125 0.24981935
#> [,8] [,9] [,10]
#> 1 2.31091578 2.37167744 2.36198559
#> 2 0.93585220 0.95791298 0.98575560
#> 3 1.41641694 1.50522613 1.47941643
#> 4 0.59950218 0.70071774 0.71423505
#> 5 0.37190673 0.42675025 0.42402377
#> 6 1.09060470 1.11874494 1.08702344
#> 7 0.09571100 0.09647244 0.09630241
#> 8 0.20785619 0.16125305 0.14088436
#> 9 2.71378257 2.69868104 2.70464247
#> 10 0.73029614 0.65922545 0.60326641
#> 11 0.54022679 0.51472598 0.49819043
#> 12 0.35506281 0.45808959 0.46151119
#> 13 2.79802911 2.76063955 2.78066411
#> 14 1.98170008 1.95001758 1.93229013
#> 15 1.53052186 1.51841242 1.52042283
#> 16 0.02766734 0.04128363 0.04540010
#> 17 0.14200686 0.10908344 0.10682024
#> 18 1.11860371 0.97590985 0.97740363
#> 19 0.58502710 0.55929325 0.55484711
#> 20 0.10381511 0.10249376 0.10126389
#> 21 0.10867268 0.22973117 0.29869098
#> 22 0.33080890 0.33655123 0.35050213
#> 23 0.54181217 0.50162257 0.50663290
#> 24 0.09203855 0.13818992 0.16532608
#> 25 0.15063372 0.18519359 0.18156725
#> 26 0.65085067 0.71168397 0.76264933
#> 27 0.14727015 0.14028361 0.13815779
#> 28 0.43710022 0.39895696 0.39737902
#> 29 1.28688769 1.23785532 1.23709320
#> 30 0.62334349 0.59651237 0.59626615
#> 31 1.30383078 1.34676942 1.34563447
#> 32 0.40329953 0.46335753 0.48230863
#> 33 0.24694643 0.23442405 0.22839915
#>
PLS_glm_wvc(round(ypine),Xpine,10,modele="pls-glm-poisson", verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> 1 1.6399850 2.60506445 2.11819574 2.41837386 2.53667962 2.20251218 2.06903053
#> 2 0.7290121 0.69629584 0.86286774 0.91041443 1.03494013 1.08047292 1.04940245
#> 3 1.4007318 0.91577118 0.87914923 0.91048321 0.88943352 1.13236954 1.27356987
#> 4 0.5073121 0.58670097 0.49153953 0.47288217 0.47195939 0.58602584 0.68553431
#> 5 0.7167976 0.26869425 0.17547231 0.16659766 0.14906365 0.20126291 0.20750054
#> 6 1.2277121 0.87836123 0.79196664 0.74357948 0.62570190 0.70241020 0.67883278
#> 7 0.2394851 0.11484061 0.10723855 0.09952527 0.09561722 0.09178740 0.10346041
#> 8 0.4486498 0.21249182 0.25417881 0.29270841 0.19704511 0.07643748 0.06392087
#> 9 1.5375425 1.72997866 1.88584119 2.06164431 2.14385483 2.24654776 2.35711461
#> 10 0.7082187 0.70892020 0.65884300 0.68326209 0.48035002 0.29285649 0.30175497
#> 11 0.2936374 0.73061296 0.24263912 0.23403044 0.20470584 0.23168872 0.22840093
#> 12 0.1322042 0.56376787 0.95488027 1.07792637 0.91182710 0.93220801 0.97645652
#> 13 2.3010540 3.38048229 3.41269178 3.09515690 3.11675468 2.83446755 2.94222186
#> 14 1.3291560 1.58321807 1.75503319 1.50912761 1.35057561 1.76448864 1.65562250
#> 15 1.1448439 1.08877131 1.25968183 1.27173335 1.27622471 1.76272105 1.72201423
#> 16 0.0941790 0.08628985 0.09230834 0.08013583 0.07468272 0.04857939 0.04622128
#> 17 0.1757439 0.12211384 0.07899382 0.05819776 0.05831164 0.05675775 0.05570430
#> 18 0.7307741 0.57135156 0.43624010 0.56728509 0.74050220 0.81621686 0.79616814
#> 19 0.8036405 0.39016521 0.60474619 0.63620204 0.64631151 0.57833457 0.61425877
#> 20 0.2296892 0.13681561 0.13816133 0.13921219 0.12832775 0.08814754 0.08562427
#> 21 0.1690309 0.41209378 0.59658496 0.47032464 0.67844078 0.64008147 0.65138413
#> 22 0.4128838 0.44811365 0.42513686 0.40081655 0.45309870 0.36374930 0.36640901
#> 23 0.6719322 0.38499657 0.43763564 0.42067876 0.42831773 0.38428972 0.34730722
#> 24 0.2518950 0.26446399 0.40504213 0.33075720 0.42496338 0.22271054 0.21188843
#> 25 0.3415030 0.09895810 0.09308877 0.09857795 0.07905177 0.08172748 0.07994832
#> 26 0.6718422 0.41760796 0.39983789 0.41407059 0.51103696 0.75685751 0.71134635
#> 27 0.1598578 0.23017899 0.06417506 0.04623807 0.03896774 0.08594351 0.07861887
#> 28 1.6103573 0.64304401 0.62041835 0.58564249 0.58885702 0.26270837 0.26265212
#> 29 1.4503379 1.50168396 1.65079230 1.65474205 1.64013824 1.36785798 1.36596556
#> 30 0.4619784 0.46051634 0.26232493 0.28912455 0.31006305 0.21938924 0.23797224
#> 31 0.7331324 0.89331268 1.39212273 1.45116888 1.34444176 1.50249618 1.40404240
#> 32 0.4547769 0.70774332 0.33469867 0.29588706 0.26928216 0.24247816 0.22480412
#> 33 0.2201032 0.16657890 0.11747299 0.11349275 0.10047155 0.14341775 0.14484710
#> [,8] [,9] [,10]
#> 1 2.02482801 2.09337142 2.09445057
#> 2 1.02979098 1.00523770 1.00859344
#> 3 1.24588728 1.28630576 1.28831559
#> 4 0.67844519 0.67808489 0.67912103
#> 5 0.20598501 0.22263909 0.22289776
#> 6 0.67635225 0.70420679 0.70561084
#> 7 0.10133294 0.09847205 0.09737196
#> 8 0.06139630 0.06185310 0.06202701
#> 9 2.43317037 2.42608241 2.43213747
#> 10 0.31175798 0.31302351 0.31392542
#> 11 0.23326785 0.22472545 0.22453972
#> 12 0.96450569 0.96172419 0.95912503
#> 13 2.92826816 2.88041181 2.87784898
#> 14 1.63943166 1.61203045 1.61806746
#> 15 1.72636185 1.72600708 1.71786827
#> 16 0.04614683 0.04911642 0.04884304
#> 17 0.05209752 0.04344071 0.04389911
#> 18 0.78681869 0.75517340 0.75514721
#> 19 0.60876974 0.59789477 0.59656484
#> 20 0.08364279 0.08252597 0.08207915
#> 21 0.65597011 0.69554797 0.69687802
#> 22 0.35829121 0.33954857 0.33920155
#> 23 0.35016186 0.33960578 0.34056020
#> 24 0.21327505 0.21575584 0.21648404
#> 25 0.07937426 0.08865757 0.08919000
#> 26 0.72799199 0.73683412 0.73410737
#> 27 0.07702918 0.07296722 0.07209219
#> 28 0.26006772 0.26199357 0.26110683
#> 29 1.38309722 1.38333502 1.37253094
#> 30 0.24610086 0.23542502 0.23820983
#> 31 1.42270195 1.41766315 1.42184343
#> 32 0.24013435 0.24834876 0.24777468
#> 33 0.14754717 0.14199043 0.14158704
#>
PLS_glm_wvc(round(ypine),Xpine,10,modele="pls-glm-family",family=poisson(log), verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> 1 1.6399850 2.60506445 2.11819574 2.41837386 2.53667962 2.20251218 2.06903053
#> 2 0.7290121 0.69629584 0.86286774 0.91041443 1.03494013 1.08047292 1.04940245
#> 3 1.4007318 0.91577118 0.87914923 0.91048321 0.88943352 1.13236954 1.27356987
#> 4 0.5073121 0.58670097 0.49153953 0.47288217 0.47195939 0.58602584 0.68553431
#> 5 0.7167976 0.26869425 0.17547231 0.16659766 0.14906365 0.20126291 0.20750054
#> 6 1.2277121 0.87836123 0.79196664 0.74357948 0.62570190 0.70241020 0.67883278
#> 7 0.2394851 0.11484061 0.10723855 0.09952527 0.09561722 0.09178740 0.10346041
#> 8 0.4486498 0.21249182 0.25417881 0.29270841 0.19704511 0.07643748 0.06392087
#> 9 1.5375425 1.72997866 1.88584119 2.06164431 2.14385483 2.24654776 2.35711461
#> 10 0.7082187 0.70892020 0.65884300 0.68326209 0.48035002 0.29285649 0.30175497
#> 11 0.2936374 0.73061296 0.24263912 0.23403044 0.20470584 0.23168872 0.22840093
#> 12 0.1322042 0.56376787 0.95488027 1.07792637 0.91182710 0.93220801 0.97645652
#> 13 2.3010540 3.38048229 3.41269178 3.09515690 3.11675468 2.83446755 2.94222186
#> 14 1.3291560 1.58321807 1.75503319 1.50912761 1.35057561 1.76448864 1.65562250
#> 15 1.1448439 1.08877131 1.25968183 1.27173335 1.27622471 1.76272105 1.72201423
#> 16 0.0941790 0.08628985 0.09230834 0.08013583 0.07468272 0.04857939 0.04622128
#> 17 0.1757439 0.12211384 0.07899382 0.05819776 0.05831164 0.05675775 0.05570430
#> 18 0.7307741 0.57135156 0.43624010 0.56728509 0.74050220 0.81621686 0.79616814
#> 19 0.8036405 0.39016521 0.60474619 0.63620204 0.64631151 0.57833457 0.61425877
#> 20 0.2296892 0.13681561 0.13816133 0.13921219 0.12832775 0.08814754 0.08562427
#> 21 0.1690309 0.41209378 0.59658496 0.47032464 0.67844078 0.64008147 0.65138413
#> 22 0.4128838 0.44811365 0.42513686 0.40081655 0.45309870 0.36374930 0.36640901
#> 23 0.6719322 0.38499657 0.43763564 0.42067876 0.42831773 0.38428972 0.34730722
#> 24 0.2518950 0.26446399 0.40504213 0.33075720 0.42496338 0.22271054 0.21188843
#> 25 0.3415030 0.09895810 0.09308877 0.09857795 0.07905177 0.08172748 0.07994832
#> 26 0.6718422 0.41760796 0.39983789 0.41407059 0.51103696 0.75685751 0.71134635
#> 27 0.1598578 0.23017899 0.06417506 0.04623807 0.03896774 0.08594351 0.07861887
#> 28 1.6103573 0.64304401 0.62041835 0.58564249 0.58885702 0.26270837 0.26265212
#> 29 1.4503379 1.50168396 1.65079230 1.65474205 1.64013824 1.36785798 1.36596556
#> 30 0.4619784 0.46051634 0.26232493 0.28912455 0.31006305 0.21938924 0.23797224
#> 31 0.7331324 0.89331268 1.39212273 1.45116888 1.34444176 1.50249618 1.40404240
#> 32 0.4547769 0.70774332 0.33469867 0.29588706 0.26928216 0.24247816 0.22480412
#> 33 0.2201032 0.16657890 0.11747299 0.11349275 0.10047155 0.14341775 0.14484710
#> [,8] [,9] [,10]
#> 1 2.02482801 2.09337142 2.09445057
#> 2 1.02979098 1.00523770 1.00859344
#> 3 1.24588728 1.28630576 1.28831559
#> 4 0.67844519 0.67808489 0.67912103
#> 5 0.20598501 0.22263909 0.22289776
#> 6 0.67635225 0.70420679 0.70561084
#> 7 0.10133294 0.09847205 0.09737196
#> 8 0.06139630 0.06185310 0.06202701
#> 9 2.43317037 2.42608241 2.43213747
#> 10 0.31175798 0.31302351 0.31392542
#> 11 0.23326785 0.22472545 0.22453972
#> 12 0.96450569 0.96172419 0.95912503
#> 13 2.92826816 2.88041181 2.87784898
#> 14 1.63943166 1.61203045 1.61806746
#> 15 1.72636185 1.72600708 1.71786827
#> 16 0.04614683 0.04911642 0.04884304
#> 17 0.05209752 0.04344071 0.04389911
#> 18 0.78681869 0.75517340 0.75514721
#> 19 0.60876974 0.59789477 0.59656484
#> 20 0.08364279 0.08252597 0.08207915
#> 21 0.65597011 0.69554797 0.69687802
#> 22 0.35829121 0.33954857 0.33920155
#> 23 0.35016186 0.33960578 0.34056020
#> 24 0.21327505 0.21575584 0.21648404
#> 25 0.07937426 0.08865757 0.08919000
#> 26 0.72799199 0.73683412 0.73410737
#> 27 0.07702918 0.07296722 0.07209219
#> 28 0.26006772 0.26199357 0.26110683
#> 29 1.38309722 1.38333502 1.37253094
#> 30 0.24610086 0.23542502 0.23820983
#> 31 1.42270195 1.41766315 1.42184343
#> 32 0.24013435 0.24834876 0.24777468
#> 33 0.14754717 0.14199043 0.14158704
#>
rm(list=c("pine","ypine","Xpine"))
#> Warning: object 'pine' not found
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_glm_wvc(yCornell,XCornell,10,modele="pls-glm-inverse.gaussian", verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 95.04599 96.84573 97.43211 97.72960 97.67692 97.66926
#> 2 96.68764 98.13364 98.03691 97.94523 98.18530 98.04886
#> 3 96.09893 97.85843 97.65344 97.12746 96.98194 97.11752
#> 4 95.02359 91.98294 91.88913 91.79131 91.91479 91.89828
#> 5 87.87811 86.94625 86.08537 86.10526 86.02670 86.01328
#> 6 93.46390 90.91980 91.39057 91.61375 91.49733 91.58550
#> 7 81.89068 81.77545 81.85201 81.95061 82.04635 82.07739
#> 8 82.40022 82.61020 82.63964 82.58216 82.62615 82.70790
#> 9 82.17002 82.45931 82.55178 82.57494 82.62086 82.69025
#> 10 82.58963 83.07833 83.11779 83.00651 83.01600 83.12446
#> 11 82.15442 81.74862 82.05216 81.73070 81.63852 81.40000
#> 12 87.59686 88.64130 88.29910 88.84247 88.76913 88.66730
#>
PLS_glm_wvc(yCornell,XCornell,10,modele="pls-glm-family",
family=inverse.gaussian(), verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 95.04599 96.84573 97.43211 97.72960 97.67692 97.66926
#> 2 96.68764 98.13364 98.03691 97.94523 98.18530 98.04886
#> 3 96.09893 97.85843 97.65344 97.12746 96.98194 97.11752
#> 4 95.02359 91.98294 91.88913 91.79131 91.91479 91.89828
#> 5 87.87811 86.94625 86.08537 86.10526 86.02670 86.01328
#> 6 93.46390 90.91980 91.39057 91.61375 91.49733 91.58550
#> 7 81.89068 81.77545 81.85201 81.95061 82.04635 82.07739
#> 8 82.40022 82.61020 82.63964 82.58216 82.62615 82.70790
#> 9 82.17002 82.45931 82.55178 82.57494 82.62086 82.69025
#> 10 82.58963 83.07833 83.11779 83.00651 83.01600 83.12446
#> 11 82.15442 81.74862 82.05216 81.73070 81.63852 81.40000
#> 12 87.59686 88.64130 88.29910 88.84247 88.76913 88.66730
#>
rm(list=c("XCornell","yCornell"))
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_glm_wvc(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,], verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3]
#> 1 95.03164 97.08409 97.4436
#>
PLS_glm_wvc(dataY=yCornell[-1],dataX=XCornell[-1,],nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,], verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3]
#> 1 93.74777 95.32475 96.08522
#>
rm("XCornell","yCornell")
data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
PLS_glm(yaze_compl,Xaze_compl,10,modele="pls-glm-logistic",typeVC="none", verbose=FALSE)$InfCrit
#> AIC BIC Missclassed Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 145.8283 148.4727 49 104.00000 25.91346 NA
#> Nb_Comp_1 118.1398 123.4285 28 100.53823 19.32272 0.2543365
#> Nb_Comp_2 109.9553 117.8885 26 99.17955 17.33735 0.3309519
#> Nb_Comp_3 105.1591 115.7366 22 123.37836 15.58198 0.3986915
#> Nb_Comp_4 103.8382 117.0601 21 114.77551 15.14046 0.4157299
#> Nb_Comp_5 104.7338 120.6001 21 105.35382 15.08411 0.4179043
#> Nb_Comp_6 105.6770 124.1878 21 98.87767 14.93200 0.4237744
#> Nb_Comp_7 107.2828 128.4380 20 97.04072 14.87506 0.4259715
#> Nb_Comp_8 109.0172 132.8167 22 98.90110 14.84925 0.4269676
#> Nb_Comp_9 110.9354 137.3793 21 100.35563 14.84317 0.4272022
#> Nb_Comp_10 112.9021 141.9904 20 102.85214 14.79133 0.4292027
#> R2_residY RSS_residY
#> Nb_Comp_0 NA 25.91346
#> Nb_Comp_1 -6.004879 181.52066
#> Nb_Comp_2 -9.617595 275.13865
#> Nb_Comp_3 -12.332217 345.48389
#> Nb_Comp_4 -15.496383 427.47839
#> Nb_Comp_5 -15.937183 438.90105
#> Nb_Comp_6 -16.700929 458.69233
#> Nb_Comp_7 -16.908851 464.08033
#> Nb_Comp_8 -17.555867 480.84675
#> Nb_Comp_9 -17.834439 488.06552
#> Nb_Comp_10 -17.999267 492.33678
PLS_glm_wvc(yaze_compl,Xaze_compl,10,modele="pls-glm-logistic", keepcoeffs=TRUE, verbose=FALSE)
#> $valsPredict
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 0.43119151 0.702047114 0.631858261 0.673867317 0.633105720 0.643502088
#> 2 0.21927519 0.208200622 0.166440274 0.108288423 0.122814698 0.163115357
#> 3 0.04106998 0.029963092 0.006791838 0.002965336 0.003725325 0.003746401
#> 4 0.30929185 0.554282284 0.414180215 0.441249985 0.520547128 0.604562658
#> 5 0.05118382 0.067233546 0.022736369 0.006104612 0.005217977 0.002943517
#> 6 0.05290489 0.111062277 0.030654567 0.012385420 0.011599399 0.007397041
#> 7 0.04770848 0.012461554 0.009969069 0.011387816 0.019758711 0.057213719
#> 8 0.23030599 0.203968184 0.120876571 0.103096864 0.048403176 0.031122482
#> 9 0.79603342 0.646912665 0.498819889 0.545037945 0.543238786 0.423550027
#> 10 0.18420109 0.281904502 0.319927429 0.355711963 0.419764286 0.422912933
#> 11 0.86850308 0.895570974 0.856803244 0.853618586 0.877371706 0.906873854
#> 12 0.81973108 0.742594700 0.819314935 0.772099212 0.770185219 0.741949824
#> 13 0.10378096 0.034374513 0.061664137 0.105513481 0.144646667 0.124182122
#> 14 0.24065500 0.216121428 0.236771150 0.254073363 0.180029130 0.162304231
#> 15 0.25406843 0.189921149 0.122309327 0.046077032 0.051655065 0.046711552
#> 16 0.11742108 0.026636030 0.021144841 0.009932451 0.011928074 0.012867736
#> 17 0.62187722 0.356241370 0.300877326 0.222486735 0.223971302 0.259218578
#> 18 0.63085304 0.495186298 0.710135221 0.596235794 0.529760711 0.488161297
#> 19 0.15384411 0.299159188 0.246846331 0.150702653 0.120019849 0.138670219
#> 20 0.23938183 0.102233996 0.261323652 0.418478866 0.480248654 0.437149596
#> 21 0.31843739 0.359915314 0.702415128 0.553592625 0.486242603 0.471686531
#> 22 0.26948160 0.218115310 0.330039231 0.176032976 0.142744996 0.144657867
#> 23 0.38688875 0.322522571 0.194282977 0.216329749 0.180302487 0.177187916
#> 24 0.25929423 0.113805680 0.125244332 0.065622331 0.055343844 0.053635436
#> 25 0.23956717 0.451995286 0.543962067 0.419106683 0.580554368 0.530330948
#> 26 0.19444922 0.251820723 0.123966447 0.177175557 0.150373132 0.093400279
#> 27 0.70737780 0.733381154 0.462512849 0.414579021 0.345820167 0.355863574
#> 28 0.13829538 0.102635881 0.082813672 0.085265556 0.055242281 0.061020998
#> 29 0.40496093 0.233837866 0.285045914 0.369925740 0.447381566 0.509598027
#> 30 0.12221316 0.186893799 0.059111670 0.048296062 0.062374711 0.061380243
#> 31 0.36560681 0.422607656 0.224275870 0.377066732 0.416945477 0.375434519
#> 32 0.58998317 0.392983408 0.151910276 0.143422224 0.185402553 0.124318557
#> 33 0.72886354 0.793463890 0.580779612 0.545477866 0.603245033 0.464806210
#> 34 0.31934676 0.184466274 0.101014622 0.077194233 0.075925319 0.066154173
#> 35 0.23746345 0.058469811 0.031578958 0.024285467 0.016659570 0.009334068
#> 36 0.35013391 0.355313187 0.415796281 0.521253111 0.426498990 0.456656482
#> 37 0.64850050 0.375923248 0.304390434 0.224221121 0.215331147 0.211685762
#> 38 0.38755032 0.137284188 0.101796277 0.084379325 0.091286905 0.115888877
#> 39 0.27424019 0.226697218 0.139175059 0.183685469 0.193530386 0.216586409
#> 40 0.65388494 0.519887626 0.478567247 0.400516903 0.394105599 0.554735843
#> 41 0.12946450 0.097012345 0.102677452 0.045309845 0.068832652 0.045887603
#> 42 0.66689831 0.490170524 0.514407085 0.617870569 0.618113005 0.602192147
#> 43 0.34774313 0.607127514 0.681870162 0.723512977 0.612203171 0.599031952
#> 44 0.20534214 0.060514691 0.034936078 0.023923047 0.022811279 0.030127494
#> 45 0.10787137 0.110762018 0.078045740 0.062754149 0.056126233 0.054466980
#> 46 0.66116234 0.725582784 0.700422712 0.842037688 0.798801727 0.748214349
#> 47 0.17500187 0.181257265 0.147033836 0.075688012 0.052217574 0.054286615
#> 48 0.27826924 0.312332943 0.156648719 0.138061479 0.102556222 0.094175338
#> 49 0.67906352 0.518547155 0.484272837 0.608913994 0.596609568 0.610163418
#> 50 0.40298084 0.482554614 0.223122702 0.198767758 0.288197158 0.261541091
#> 51 0.28408032 0.163890090 0.188384267 0.161186131 0.189991487 0.226812553
#> 52 0.02120634 0.007494379 0.010418897 0.005706003 0.004028951 0.003211954
#> 53 0.53874100 0.450203729 0.614709704 0.386004761 0.328383647 0.295044939
#> 54 0.67152137 0.406158600 0.409417001 0.411703449 0.449462796 0.485883982
#> 55 0.08777970 0.075522741 0.132824654 0.178966392 0.124633556 0.141516794
#> 56 0.61855060 0.721686274 0.584022743 0.783926814 0.737841421 0.696846758
#> 57 0.52725323 0.671536671 0.902964122 0.912756588 0.928084614 0.938575849
#> 58 0.67512847 0.587075631 0.694863898 0.715487331 0.707353396 0.646163105
#> 59 0.39617134 0.436010575 0.346619901 0.369125532 0.274518456 0.240623252
#> 60 0.20362696 0.227843791 0.394195242 0.515590659 0.637767577 0.535068332
#> 61 0.67200417 0.660036839 0.747335781 0.662234187 0.679349829 0.720216240
#> 62 0.50396071 0.635184174 0.695678303 0.564600033 0.608155992 0.607249320
#> 63 0.53548611 0.537052534 0.518321788 0.506052818 0.560106390 0.705117054
#> 64 0.81456207 0.923746901 0.940505447 0.946852982 0.937110807 0.963914099
#> 65 0.76930107 0.652078773 0.779696124 0.750240414 0.759297145 0.774107137
#> 66 0.74081281 0.538838825 0.490449388 0.561092475 0.628698596 0.645415477
#> 67 0.91280565 0.985524499 0.987385270 0.995952381 0.995809795 0.994173319
#> 68 0.27025468 0.642218762 0.881003868 0.892387298 0.893777605 0.913554112
#> 69 0.72400506 0.847099614 0.763912533 0.755718333 0.751986129 0.842526567
#> 70 0.75276334 0.922407667 0.915955573 0.924372638 0.952274824 0.956119967
#> 71 0.50141585 0.318030343 0.310744148 0.218311644 0.212515092 0.174944911
#> 72 0.12927414 0.099124892 0.030697353 0.026288318 0.034633861 0.054380083
#> 73 0.48132481 0.465376787 0.500862912 0.742159597 0.688353402 0.689246028
#> 74 0.68772032 0.778090832 0.874976023 0.935754083 0.949673750 0.969407185
#> 75 0.72031937 0.606769993 0.622494495 0.572143218 0.579477288 0.627568141
#> 76 0.55793917 0.494421342 0.437434284 0.460400091 0.460542480 0.494890446
#> 77 0.79943991 0.855257753 0.935928508 0.978005670 0.981273972 0.984309678
#> 78 0.85745937 0.830618813 0.865134636 0.898312327 0.913928051 0.928170413
#> 79 0.83345117 0.919224715 0.856369203 0.791325587 0.745470889 0.766729075
#> 80 0.57235513 0.414039021 0.351476404 0.311958419 0.290518135 0.263925112
#> 81 0.86879852 0.898679681 0.879900894 0.773949255 0.704553708 0.671359206
#> 82 0.45535619 0.764274615 0.797532046 0.744461196 0.824764221 0.811884622
#> 83 0.82827167 0.900830434 0.938275422 0.933835357 0.951546879 0.956093466
#> 84 0.70805640 0.903075834 0.967809228 0.979031867 0.972328032 0.963104641
#> 85 0.58750948 0.270646003 0.275457584 0.262474288 0.265058291 0.259707331
#> 86 0.85501392 0.950653792 0.950972848 0.957421636 0.940415633 0.911863862
#> 87 0.59350862 0.367675846 0.541557780 0.623191067 0.628609893 0.603566405
#> 88 0.78701299 0.779778499 0.528980639 0.595241939 0.624596722 0.612263975
#> 89 0.53053475 0.463363952 0.357941654 0.342680704 0.325066419 0.411465198
#> 90 0.78742155 0.970653016 0.964721638 0.978839943 0.985003070 0.987145933
#> 91 0.35243467 0.320053491 0.479839722 0.640766735 0.611879896 0.666491216
#> 92 0.89720093 0.966418149 0.960220418 0.963882817 0.955612296 0.958551535
#> 93 0.68862173 0.907543424 0.913645809 0.913758045 0.951448175 0.959000320
#> 94 0.28015169 0.404075368 0.545346353 0.760763289 0.680088744 0.672242170
#> 95 0.47946796 0.826642022 0.699988396 0.535143591 0.588217592 0.502063440
#> 96 0.29303531 0.260466286 0.575477371 0.479152176 0.513734415 0.428690041
#> 97 0.57134153 0.493862376 0.662480104 0.534413997 0.483990436 0.485013225
#> 98 0.80738973 0.800712471 0.861510658 0.899175983 0.915135265 0.882660615
#> 99 0.17424886 0.084872199 0.039330110 0.089167782 0.089911231 0.075436072
#> 100 0.69464297 0.849056789 0.901977691 0.852790391 0.855378024 0.837083296
#> 101 0.42957598 0.523749819 0.697708069 0.781939401 0.831423860 0.869246953
#> 102 0.77532567 0.726430234 0.850516228 0.849288906 0.876686813 0.866961583
#> 103 0.30244539 0.633632746 0.580168854 0.600867278 0.516381330 0.595553649
#> 104 0.72830077 0.856329971 0.822297122 0.839564063 0.843377821 0.894234435
#> [,7] [,8] [,9] [,10]
#> 1 0.638787122 0.659736147 0.6929134622 0.6943989562
#> 2 0.183028361 0.177705421 0.1926067471 0.1927561148
#> 3 0.003376060 0.003152591 0.0032092459 0.0031900703
#> 4 0.637363116 0.601345955 0.6315525475 0.6128454701
#> 5 0.001804324 0.001087005 0.0009059437 0.0008702787
#> 6 0.007558967 0.008124069 0.0075922059 0.0072190675
#> 7 0.075010051 0.090577306 0.0904145007 0.0948080986
#> 8 0.037693727 0.036957495 0.0385194884 0.0366579947
#> 9 0.425135095 0.416115302 0.4234960049 0.4324032991
#> 10 0.343557178 0.355791649 0.3338844672 0.3416736098
#> 11 0.886266198 0.869985302 0.8690005512 0.8662229884
#> 12 0.710054194 0.673535568 0.6614385720 0.6495800417
#> 13 0.113655657 0.099642192 0.0950823806 0.0871083484
#> 14 0.162253329 0.157798411 0.1528392913 0.1395625569
#> 15 0.052536503 0.054898188 0.0458127882 0.0418375835
#> 16 0.014844229 0.016856869 0.0175371209 0.0173133552
#> 17 0.265167590 0.277072182 0.2975291066 0.3102814947
#> 18 0.471171553 0.456928590 0.4615499112 0.4590135395
#> 19 0.124799505 0.110005858 0.0939806189 0.0901182975
#> 20 0.390670737 0.441849813 0.4361859267 0.4353931743
#> 21 0.476731092 0.500960968 0.5071133610 0.5064639572
#> 22 0.122666126 0.121059180 0.1160295322 0.1176406817
#> 23 0.157621354 0.130954478 0.1380573789 0.1433585464
#> 24 0.060662413 0.064337031 0.0732478070 0.0788979344
#> 25 0.578979810 0.545258742 0.5619282569 0.5595832463
#> 26 0.108427152 0.149040860 0.1378520800 0.1466056822
#> 27 0.395406681 0.355201942 0.3518622723 0.3491848585
#> 28 0.063474701 0.061968018 0.0571164691 0.0556497892
#> 29 0.437293949 0.440015737 0.4520380190 0.4550505243
#> 30 0.090222972 0.106807614 0.1110660966 0.1078204935
#> 31 0.352227836 0.338481138 0.3335947194 0.3461925622
#> 32 0.137231222 0.139858851 0.1304182629 0.1232302292
#> 33 0.429294436 0.474754408 0.4757750462 0.4534418743
#> 34 0.075702396 0.089288097 0.0976117186 0.1002116059
#> 35 0.008228014 0.007198486 0.0075462359 0.0077113361
#> 36 0.416343698 0.433478675 0.4314919966 0.4291386780
#> 37 0.214184092 0.233031978 0.2466886171 0.2451334330
#> 38 0.131467992 0.133488355 0.1335614164 0.1313510107
#> 39 0.155762668 0.148717710 0.1567326194 0.1594062043
#> 40 0.577240742 0.554673059 0.5497730900 0.5509835451
#> 41 0.048124309 0.050731059 0.0500083553 0.0448639866
#> 42 0.557924005 0.621291699 0.6152093088 0.6041143234
#> 43 0.634717435 0.625848315 0.6179020932 0.6180509570
#> 44 0.030924644 0.034659867 0.0306744277 0.0329719302
#> 45 0.044139143 0.041342057 0.0370482993 0.0355143768
#> 46 0.765226062 0.719351450 0.7165147398 0.7371329957
#> 47 0.057359793 0.045401532 0.0448929095 0.0487307228
#> 48 0.078230361 0.075182597 0.0721409234 0.0707122052
#> 49 0.694411530 0.720080600 0.7210842527 0.7183599426
#> 50 0.235732262 0.220747630 0.2261186866 0.2145370126
#> 51 0.234065793 0.220978475 0.1869430913 0.1995610601
#> 52 0.003437084 0.003779608 0.0035686761 0.0033152141
#> 53 0.303245796 0.306092227 0.3003119432 0.3125280275
#> 54 0.523115204 0.502477841 0.4860084012 0.4799221158
#> 55 0.159531646 0.148936794 0.1471387050 0.1461051611
#> 56 0.704311058 0.670809998 0.6863833984 0.6954430065
#> 57 0.943204808 0.926497752 0.9285111732 0.9189983080
#> 58 0.654185441 0.637253621 0.6339188721 0.6237031375
#> 59 0.211731291 0.194702818 0.2153012812 0.2224698451
#> 60 0.578481709 0.553430985 0.5498083458 0.5552296673
#> 61 0.717399252 0.688899651 0.6833342733 0.6938073689
#> 62 0.580236946 0.561386908 0.5344644041 0.5476964614
#> 63 0.651909358 0.666423048 0.6518698084 0.6663502131
#> 64 0.966570975 0.967919914 0.9629859429 0.9607163779
#> 65 0.769033500 0.771933407 0.7688031852 0.7654739625
#> 66 0.618660601 0.605251254 0.6147034915 0.6115411360
#> 67 0.992233615 0.992370008 0.9912205187 0.9908677967
#> 68 0.926820425 0.940010587 0.9433622300 0.9472604786
#> 69 0.820022341 0.828019425 0.8345071602 0.8307300622
#> 70 0.952446809 0.942761722 0.9464573655 0.9474262795
#> 71 0.177146497 0.202296797 0.2065928146 0.2148691609
#> 72 0.059294591 0.044850251 0.0396625181 0.0359318012
#> 73 0.764615989 0.774200149 0.7720125792 0.7645581755
#> 74 0.977761403 0.984747935 0.9840257594 0.9833411230
#> 75 0.691909459 0.682556639 0.6572532436 0.6429766278
#> 76 0.532574707 0.494727136 0.4906445050 0.5066664293
#> 77 0.987637116 0.991228314 0.9916479530 0.9915530258
#> 78 0.945274182 0.949158918 0.9500366289 0.9503794757
#> 79 0.754986019 0.776998067 0.7816242563 0.7871699651
#> 80 0.228016301 0.223396570 0.2186964646 0.2082883519
#> 81 0.666420005 0.684833773 0.6766117373 0.6632488136
#> 82 0.801528750 0.829859317 0.8267776343 0.8261734713
#> 83 0.959791502 0.967581315 0.9699116096 0.9732698475
#> 84 0.956332714 0.964097103 0.9633606742 0.9650418710
#> 85 0.265579761 0.306826150 0.3330031055 0.3383605790
#> 86 0.891734459 0.888000574 0.8923024430 0.8958113953
#> 87 0.634861265 0.637836802 0.6497185838 0.6508400647
#> 88 0.592698628 0.583637434 0.5435709244 0.5588191361
#> 89 0.424076781 0.404623342 0.4313744898 0.4218769380
#> 90 0.989332252 0.992616886 0.9943134281 0.9946703862
#> 91 0.675576650 0.734303755 0.7206461704 0.7140897952
#> 92 0.953076020 0.959646001 0.9545742998 0.9480225668
#> 93 0.970105937 0.974883444 0.9754998983 0.9782804092
#> 94 0.680953582 0.649567373 0.6240201397 0.6168279359
#> 95 0.561170807 0.599211294 0.5856984481 0.5961543896
#> 96 0.396894305 0.339334143 0.3662679214 0.3640990048
#> 97 0.484857170 0.487769044 0.4938813223 0.4960597646
#> 98 0.881013093 0.859741343 0.8710430696 0.8736903224
#> 99 0.072463010 0.082375646 0.0886148007 0.0861258464
#> 100 0.853833336 0.877254170 0.8697760323 0.8733522761
#> 101 0.873059397 0.868378502 0.8660074209 0.8659618513
#> 102 0.877121140 0.890714101 0.8995141757 0.9033306611
#> 103 0.555036219 0.587526765 0.6104774849 0.6020532420
#> 104 0.871930923 0.882906865 0.8840853461 0.8836606796
#>
#> $coeffs
#> [,1]
#> tempConstante -2.276982302
#> -1.068275295
#> 3.509231595
#> -1.651869135
#> 2.207538418
#> 0.568523938
#> -0.059691869
#> -0.214529856
#> -1.405223273
#> 0.396973880
#> -0.782167532
#> 0.677591817
#> -0.972259676
#> 0.650745841
#> 0.723667343
#> 0.477540145
#> 0.638755948
#> 1.666070158
#> -0.005938234
#> 0.482766293
#> -0.904425334
#> 0.300460249
#> 1.367992779
#> -1.201977825
#> -1.536120691
#> -1.983144986
#> 1.544435411
#> 1.410302156
#> -0.495400138
#> 0.454129717
#> 1.240250301
#> -0.222933455
#> -2.822712745
#> 0.026369914
#>
rm("Xaze_compl","yaze_compl")
# }