Light version of PLS_glm for cross validation purposes either on
complete or incomplete datasets.
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
)response (training) dataset
predictor(s) (training) dataset
number of components to be extracted
predictor(s) (testing) dataset
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 the family option.
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 family for details of family functions.) To use
the family option, please set modele="pls-glm-family". User defined
families can also be defined. See details.
scale the predictor(s) : must be set to TRUE for
modele="pls" and should be for glms pls.
scale the response : Yes/No. Ignored since non always possible for glm responses.
whether the coefficients of the linear fit on link scale of unstandardized eXplanatory variables should be returned or not.
whether the coefficients of the linear fit on link scale of standardized eXplanatory variables should be returned or not.
minimal value for Norm2(Xi) and \(\mathrm{det}(pp' \times
pp)\) if there is any missing value in the dataX. It
defaults to \(10^{-12}\)
an optional vector of 'prior weights' to be used in the
fitting process. Should be NULL or a numeric vector.
logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
should info messages be displayed ?
nrow(dataPredictY) * nt matrix of the
predicted values
If the coefficients of the
eXplanatory variables were requested:
i.e. keepcoeffs=TRUE.ncol(dataX) * 1 matrix of the coefficients of the the eXplanatory
variables
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 :
ordinary pls models
glm gaussian with inverse link pls models
glm gaussian with identity link pls models
glm binomial with square inverse link pls models
glm binomial with logit link pls models
glm poisson with log link pls models
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.
accepts
the links (as names) identity, log and
inverse.
accepts the links (as names)
identity, log and inverse.
accepts the
links (as names) identity, log and inverse.
accepts the links logit, probit, cauchit,
(corresponding to logistic, normal and Cauchy CDFs respectively) log
and cloglog (complementary log-log).
accepts
the links logit, probit, cauchit, (corresponding to
logistic, normal and Cauchy CDFs respectively) log and cloglog
(complementary log-log).
accepts the links logit,
probit, cauchit, (corresponding to logistic, normal and Cauchy
CDFs respectively) log and cloglog (complementary log-log).
accepts the links inverse, identity and
log.
accepts the links inverse,
identity and log.
accepts the links
inverse, identity and log.
accepts the
links log, identity, and
sqrt.
accepts the links log,
identity, and sqrt.
accepts the links
log, identity, and sqrt.
accepts the links
1/mu^2, inverse, identity and
log.
accepts the links 1/mu^2,
inverse, identity and log.
accepts the
links 1/mu^2, inverse, identity and log.
accepts the links logit, probit, cloglog,
identity, inverse, log, 1/mu^2 and
sqrt.
accepts the links logit,
probit, cloglog, identity, inverse, log,
1/mu^2 and sqrt.
accepts the links
logit, probit, cloglog, identity,
inverse, log, 1/mu^2 and sqrt.
can be used to create a power link function.
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.
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
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
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")
# }