This function provides a predict method for the class "plsRglmmodel"
Arguments
- object
An object of the class
"plsRmodel".- newdata
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
- comps
A value with a single value of component to use for prediction.
- type
Type of predicted value. Available choices are the glms ones ("
link", "response", "terms"), the polr ones ("class", "probs") or the scores ("scores").- se.fit
If TRUE, pointwise standard errors are produced for the predictions using the Cox model.
- weights
Vector of case weights. If
weightsis a vector of integers, then the estimated coefficients are equivalent to estimating the model from data with the individualcasesreplicated as many times as indicated byweights.- dispersion
the dispersion of the GLM fit to be assumed in computing the standard errors. If omitted, that returned by summary applied to the object is used.
- methodNA
Selects the way of predicting the response or the scores of the new data. For complete rows, without any missing value, there are two different ways of computing the prediction. As a consequence, for mixed datasets, with complete and incomplete rows, there are two ways of computing prediction : either predicts any row as if there were missing values in it (
missingdata) or selects the prediction method accordingly to the completeness of the row (adaptative).- verbose
should info messages be displayed ?
- ...
Arguments to be passed on to
stats::glmandplsRglm::plsRglm.
Value
When type is "response", a matrix of predicted response
values is returned.
When type is "scores", a score matrix is
returned.
References
Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Societe Francaise de Statistique, 151(2), pages 1-18. https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/47/
See also
See Also predict.glm
Author
Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/
Examples
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
data(pine_sup)
Xpine_sup<-pine_sup[,1:10]
Xpine_supNA<-Xpine_sup
Xpine_supNA[1,1]<-NA
modpls=plsRglm(object=ypine,dataX=Xpine,nt=6,modele="pls-glm-family",family="gaussian",
verbose=FALSE)
modplsform=plsRglm(x11~.,data=pine,nt=6,modele="pls-glm-family",family="gaussian", verbose=FALSE)
modpls2=plsRglm(object=ypine,dataX=Xpine,nt=6,modele="pls-glm-family",
dataPredictY=Xpine_sup,family="gaussian", verbose=FALSE)
modpls2NA=plsRglm(object=ypine,dataX=Xpine,nt=6,modele="pls-glm-family",
dataPredictY=Xpine_supNA,family="gaussian", verbose=FALSE)
#Identical to predict(modpls,type="link") or modpls$Std.ValsPredictY
cbind(modpls$Std.ValsPredictY,modplsform$Std.ValsPredictY,
predict(modpls),predict(modplsform))
#> [,1] [,2] [,3] [,4]
#> 1 1.9833376732 1.9833376732 1.9833376732 1.9833376732
#> 2 1.2424038010 1.2424038010 1.2424038010 1.2424038010
#> 3 1.4982947689 1.4982947689 1.4982947689 1.4982947689
#> 4 0.9909505955 0.9909505955 0.9909505955 0.9909505955
#> 5 0.5188201727 0.5188201727 0.5188201727 0.5188201727
#> 6 1.3306250748 1.3306250748 1.3306250748 1.3306250748
#> 7 -0.0207190905 -0.0207190905 -0.0207190905 -0.0207190905
#> 8 0.3578640505 0.3578640505 0.3578640505 0.3578640505
#> 9 1.6121834444 1.6121834444 1.6121834444 1.6121834444
#> 10 0.8174125637 0.8174125637 0.8174125637 0.8174125637
#> 11 0.6245784031 0.6245784031 0.6245784031 0.6245784031
#> 12 1.0296261669 1.0296261669 1.0296261669 1.0296261669
#> 13 2.1697925483 2.1697925483 2.1697925483 2.1697925483
#> 14 1.8135307368 1.8135307368 1.8135307368 1.8135307368
#> 15 1.5668160958 1.5668160958 1.5668160958 1.5668160958
#> 16 -0.4913222929 -0.4913222929 -0.4913222929 -0.4913222929
#> 17 0.0003648119 0.0003648119 0.0003648119 0.0003648119
#> 18 0.9670404542 0.9670404542 0.9670404542 0.9670404542
#> 19 0.9155210110 0.9155210110 0.9155210110 0.9155210110
#> 20 0.0147427279 0.0147427279 0.0147427279 0.0147427279
#> 21 0.5787557488 0.5787557488 0.5787557488 0.5787557488
#> 22 0.7677772238 0.7677772238 0.7677772238 0.7677772238
#> 23 0.6902564661 0.6902564661 0.6902564661 0.6902564661
#> 24 0.2269039458 0.2269039458 0.2269039458 0.2269039458
#> 25 -0.1262912840 -0.1262912840 -0.1262912840 -0.1262912840
#> 26 0.7620516870 0.7620516870 0.7620516870 0.7620516870
#> 27 0.2265845367 0.2265845367 0.2265845367 0.2265845367
#> 28 0.9429046207 0.9429046207 0.9429046207 0.9429046207
#> 29 1.5828522058 1.5828522058 1.5828522058 1.5828522058
#> 30 0.3072993447 0.3072993447 0.3072993447 0.3072993447
#> 31 1.3622819484 1.3622819484 1.3622819484 1.3622819484
#> 32 0.4779691553 0.4779691553 0.4779691553 0.4779691553
#> 33 0.0287906836 0.0287906836 0.0287906836 0.0287906836
#Identical to predict(modpls,type="response") or modpls$ValsPredictY
cbind(modpls$ValsPredictY,modplsform$ValsPredictY,
predict(modpls,type="response"),predict(modplsform,type="response"))
#> [,1] [,2] [,3] [,4]
#> 1 1.9833376732 1.9833376732 1.9833376732 1.9833376732
#> 2 1.2424038010 1.2424038010 1.2424038010 1.2424038010
#> 3 1.4982947689 1.4982947689 1.4982947689 1.4982947689
#> 4 0.9909505955 0.9909505955 0.9909505955 0.9909505955
#> 5 0.5188201727 0.5188201727 0.5188201727 0.5188201727
#> 6 1.3306250748 1.3306250748 1.3306250748 1.3306250748
#> 7 -0.0207190905 -0.0207190905 -0.0207190905 -0.0207190905
#> 8 0.3578640505 0.3578640505 0.3578640505 0.3578640505
#> 9 1.6121834444 1.6121834444 1.6121834444 1.6121834444
#> 10 0.8174125637 0.8174125637 0.8174125637 0.8174125637
#> 11 0.6245784031 0.6245784031 0.6245784031 0.6245784031
#> 12 1.0296261669 1.0296261669 1.0296261669 1.0296261669
#> 13 2.1697925483 2.1697925483 2.1697925483 2.1697925483
#> 14 1.8135307368 1.8135307368 1.8135307368 1.8135307368
#> 15 1.5668160958 1.5668160958 1.5668160958 1.5668160958
#> 16 -0.4913222929 -0.4913222929 -0.4913222929 -0.4913222929
#> 17 0.0003648119 0.0003648119 0.0003648119 0.0003648119
#> 18 0.9670404542 0.9670404542 0.9670404542 0.9670404542
#> 19 0.9155210110 0.9155210110 0.9155210110 0.9155210110
#> 20 0.0147427279 0.0147427279 0.0147427279 0.0147427279
#> 21 0.5787557488 0.5787557488 0.5787557488 0.5787557488
#> 22 0.7677772238 0.7677772238 0.7677772238 0.7677772238
#> 23 0.6902564661 0.6902564661 0.6902564661 0.6902564661
#> 24 0.2269039458 0.2269039458 0.2269039458 0.2269039458
#> 25 -0.1262912840 -0.1262912840 -0.1262912840 -0.1262912840
#> 26 0.7620516870 0.7620516870 0.7620516870 0.7620516870
#> 27 0.2265845367 0.2265845367 0.2265845367 0.2265845367
#> 28 0.9429046207 0.9429046207 0.9429046207 0.9429046207
#> 29 1.5828522058 1.5828522058 1.5828522058 1.5828522058
#> 30 0.3072993447 0.3072993447 0.3072993447 0.3072993447
#> 31 1.3622819484 1.3622819484 1.3622819484 1.3622819484
#> 32 0.4779691553 0.4779691553 0.4779691553 0.4779691553
#> 33 0.0287906836 0.0287906836 0.0287906836 0.0287906836
#Identical to modpls$ttPredictY
predict(modpls,type="scores")
#> Comp_1 Comp_2 Comp_3 Comp_4 Comp_5 Comp_6
#> 1 2.9096976 1.00710122 -0.24592387 -0.160828305 -0.176676852 -0.04612186
#> 2 0.7632932 0.15976639 0.27373619 0.140033799 -0.211959106 0.23748516
#> 3 2.5797729 -0.15255047 0.29850835 -0.463288186 0.395641948 0.06260773
#> 4 0.0443116 0.18393139 0.35088163 -0.751984567 0.503753361 0.04469555
#> 5 0.8636609 -0.71278066 -0.37239336 -0.161000543 0.039381404 -0.07357890
#> 6 2.0735510 0.01335370 -0.07197122 0.334437598 -0.101229035 -0.13598146
#> 7 -1.8665241 -0.99415606 0.33015396 -0.396546167 0.785963932 0.12857825
#> 8 -0.3528084 -0.47395114 -0.24214142 0.751121043 -0.169208869 -0.36500370
#> 9 2.5760644 0.16552577 0.04928545 -0.089535122 0.167729285 0.03063475
#> 10 0.7101307 -0.07990096 -0.15351493 -0.005071190 0.409378779 -0.43921245
#> 11 -1.4335481 1.07257765 -1.18074795 -0.436193501 0.158685019 -0.09759548
#> 12 -3.3502270 1.30698624 0.87144711 0.386088832 0.978624443 -0.05611513
#> 13 3.6223851 0.68753187 0.40988945 -0.264421225 -0.030412303 -0.06398871
#> 14 2.1481736 0.57279554 0.28246967 0.653041463 -0.327686046 0.03285982
#> 15 1.8241923 0.13197474 0.13629418 0.539338767 0.189873538 0.17720966
#> 16 -4.2769308 -0.28783227 0.30482521 0.105860327 -0.057241843 -0.30620830
#> 17 -2.7842661 -0.16567944 0.07256901 -0.315395828 -0.707648649 0.39219062
#> 18 0.8485036 -0.05290953 -0.61740825 -0.280346593 -0.011117608 0.45336039
#> 19 1.0268524 -0.80640496 0.57912902 0.064174398 0.354166437 0.14689814
#> 20 -2.0252326 -0.58395494 0.04522859 0.075864802 0.204673403 -0.01550492
#> 21 -2.9034527 0.78107186 1.11976594 -0.422652246 -0.632696644 -0.05241835
#> 22 -0.6130182 0.13878788 0.24414686 -0.354981337 -0.127986504 0.14392098
#> 23 0.3973956 -0.47715993 -0.08672426 0.577352623 -0.498808259 0.10940105
#> 24 -1.9919455 -0.11392902 0.83986464 -0.231967944 -0.889717260 -0.14566335
#> 25 -0.9692387 -1.09175820 -0.24633375 0.268105315 -0.001492138 -0.21439636
#> 26 0.4320999 -0.42853732 -0.32668909 0.438180905 -0.212606195 0.32458213
#> 27 -3.0186366 0.68892697 -1.13558954 0.357517970 0.068179402 0.20518691
#> 28 2.7834585 -0.79904726 0.11258827 -0.544298344 -0.268790892 -0.23821771
#> 29 2.4348733 0.11286629 0.15459790 0.102159012 0.238308194 -0.05212571
#> 30 -0.3039968 -0.10418524 -0.64875748 -1.092874683 -0.151109635 -0.05322880
#> 31 0.6490525 0.29413661 0.31920770 0.875287429 -0.027498146 0.04838810
#> 32 -0.5424034 0.43591326 -0.99283694 -0.002253209 -0.288501338 -0.34514010
#> 33 -2.2552401 -0.42850996 -0.47355708 0.305074707 0.398028177 0.16250207
predict(modplsform,type="scores")
#> Comp_1 Comp_2 Comp_3 Comp_4 Comp_5 Comp_6
#> 1 2.9096976 1.00710122 -0.24592387 -0.160828305 -0.176676852 -0.04612186
#> 2 0.7632932 0.15976639 0.27373619 0.140033799 -0.211959106 0.23748516
#> 3 2.5797729 -0.15255047 0.29850835 -0.463288186 0.395641948 0.06260773
#> 4 0.0443116 0.18393139 0.35088163 -0.751984567 0.503753361 0.04469555
#> 5 0.8636609 -0.71278066 -0.37239336 -0.161000543 0.039381404 -0.07357890
#> 6 2.0735510 0.01335370 -0.07197122 0.334437598 -0.101229035 -0.13598146
#> 7 -1.8665241 -0.99415606 0.33015396 -0.396546167 0.785963932 0.12857825
#> 8 -0.3528084 -0.47395114 -0.24214142 0.751121043 -0.169208869 -0.36500370
#> 9 2.5760644 0.16552577 0.04928545 -0.089535122 0.167729285 0.03063475
#> 10 0.7101307 -0.07990096 -0.15351493 -0.005071190 0.409378779 -0.43921245
#> 11 -1.4335481 1.07257765 -1.18074795 -0.436193501 0.158685019 -0.09759548
#> 12 -3.3502270 1.30698624 0.87144711 0.386088832 0.978624443 -0.05611513
#> 13 3.6223851 0.68753187 0.40988945 -0.264421225 -0.030412303 -0.06398871
#> 14 2.1481736 0.57279554 0.28246967 0.653041463 -0.327686046 0.03285982
#> 15 1.8241923 0.13197474 0.13629418 0.539338767 0.189873538 0.17720966
#> 16 -4.2769308 -0.28783227 0.30482521 0.105860327 -0.057241843 -0.30620830
#> 17 -2.7842661 -0.16567944 0.07256901 -0.315395828 -0.707648649 0.39219062
#> 18 0.8485036 -0.05290953 -0.61740825 -0.280346593 -0.011117608 0.45336039
#> 19 1.0268524 -0.80640496 0.57912902 0.064174398 0.354166437 0.14689814
#> 20 -2.0252326 -0.58395494 0.04522859 0.075864802 0.204673403 -0.01550492
#> 21 -2.9034527 0.78107186 1.11976594 -0.422652246 -0.632696644 -0.05241835
#> 22 -0.6130182 0.13878788 0.24414686 -0.354981337 -0.127986504 0.14392098
#> 23 0.3973956 -0.47715993 -0.08672426 0.577352623 -0.498808259 0.10940105
#> 24 -1.9919455 -0.11392902 0.83986464 -0.231967944 -0.889717260 -0.14566335
#> 25 -0.9692387 -1.09175820 -0.24633375 0.268105315 -0.001492138 -0.21439636
#> 26 0.4320999 -0.42853732 -0.32668909 0.438180905 -0.212606195 0.32458213
#> 27 -3.0186366 0.68892697 -1.13558954 0.357517970 0.068179402 0.20518691
#> 28 2.7834585 -0.79904726 0.11258827 -0.544298344 -0.268790892 -0.23821771
#> 29 2.4348733 0.11286629 0.15459790 0.102159012 0.238308194 -0.05212571
#> 30 -0.3039968 -0.10418524 -0.64875748 -1.092874683 -0.151109635 -0.05322880
#> 31 0.6490525 0.29413661 0.31920770 0.875287429 -0.027498146 0.04838810
#> 32 -0.5424034 0.43591326 -0.99283694 -0.002253209 -0.288501338 -0.34514010
#> 33 -2.2552401 -0.42850996 -0.47355708 0.305074707 0.398028177 0.16250207
# \donttest{
#Identical to modpls2$ValsPredictY
cbind(predict(modpls,newdata=Xpine_sup,type="response"),
predict(modplsform,newdata=Xpine_sup,type="response"))
#> [,1] [,2]
#> [1,] 1.1097381 1.1097381
#> [2,] 1.6368638 1.6368638
#> [3,] 1.4742716 1.4742716
#> [4,] 1.7447487 1.7447487
#> [5,] 1.4156388 1.4156388
#> [6,] 2.9500255 2.9500255
#> [7,] 2.0414347 2.0414347
#> [8,] 1.8312096 1.8312096
#> [9,] 1.5718912 1.5718912
#> [10,] 2.0025140 2.0025140
#> [11,] 2.0487486 2.0487486
#> [12,] 2.0535109 2.0535109
#> [13,] 1.0628496 1.0628496
#> [14,] 0.8116580 0.8116580
#> [15,] 0.1798506 0.1798506
#> [16,] 0.3632888 0.3632888
#> [17,] 0.9930354 0.9930354
#> [18,] 1.0399233 1.0399233
#> [19,] 0.2747613 0.2747613
#> [20,] 0.7296300 0.7296300
#> [21,] 1.2241840 1.2241840
#> [22,] 0.3757749 0.3757749
#> [23,] 1.2966168 1.2966168
#> [24,] 0.3423249 0.3423249
#> [25,] 0.3425252 0.3425252
#Select the number of components to use to derive the prediction
predict(modpls,newdata=Xpine_sup,type="response",comps=1)
#> [1] 0.4529386 1.5275250 0.8653393 1.2623019 1.2181767 0.5365724 1.8005533
#> [8] 1.4130804 1.3482246 0.8525246 1.4451515 1.0083162 0.1933256 0.4868117
#> [15] 0.2004471 0.1145533 0.6559225 1.0049792 0.4994408 0.4326020 0.8987432
#> [22] 0.4734407 1.0680659 0.1914079 0.6558292
predict(modpls,newdata=Xpine_sup,type="response",comps=3)
#> [1] 1.4182116 1.6412293 1.6692276 1.8131310 1.4721576 2.4824189 2.3952957
#> [8] 1.9883342 1.5249653 1.8631319 2.3120849 1.6636405 1.0800241 0.9203430
#> [15] 1.0943056 0.6916340 1.3022034 1.2067980 0.1706657 0.7408545 1.3858670
#> [22] 0.2923245 1.4945633 0.4191331 0.4186188
predict(modpls,newdata=Xpine_sup,type="response",comps=6)
#> [1] 1.1097381 1.6368638 1.4742716 1.7447487 1.4156388 2.9500255 2.0414347
#> [8] 1.8312096 1.5718912 2.0025140 2.0487486 2.0535109 1.0628496 0.8116580
#> [15] 0.1798506 0.3632888 0.9930354 1.0399233 0.2747613 0.7296300 1.2241840
#> [22] 0.3757749 1.2966168 0.3423249 0.3425252
try(predict(modpls,newdata=Xpine_sup,type="response",comps=8))
#> Error in predict.plsRglmmodel(modpls, newdata = Xpine_sup, type = "response", :
#> Cannot predict using more components than extracted.
#Identical to modpls2$ttValsPredictY
predict(modpls,newdata=Xpine_sup,type="scores")
#> Comp_1 Comp_2 Comp_3 Comp_4 Comp_5 Comp_6
#> [1,] -1.3617679 1.84853757 -0.07167989 -0.78643051 -0.55491628 -0.31285630
#> [2,] 2.7226457 -0.18222256 0.81656680 0.02721720 0.86067342 -0.37393001
#> [3,] 0.2057330 0.81889037 1.42664508 -0.15485978 0.43372368 -0.65684336
#> [4,] 1.7145547 0.87851109 0.32284764 0.37343500 0.23900014 -0.44019550
#> [5,] 1.5468388 0.06396562 0.85245128 -0.16192486 0.59815956 -0.33518539
#> [6,] -1.0438828 2.94178014 1.47387097 1.08467443 -0.02394675 0.87054622
#> [7,] 3.7604036 1.20512548 -0.18065028 -0.40642515 -0.37373716 -0.66809476
#> [8,] 2.2876515 0.79101480 0.59799022 -0.32260550 0.35381984 -0.45046964
#> [9,] 2.0411399 0.23491526 0.20046643 0.46551988 0.41574301 -0.22843097
#> [10,] 0.1570251 1.48472812 0.85445008 0.03281035 0.58589348 0.13631629
#> [11,] 2.4095509 1.50815475 0.24927003 -1.08425752 0.08663564 -0.32703134
#> [12,] 0.7491762 1.04259904 0.38939526 1.71739480 -0.33430079 0.52239057
#> [13,] -2.3485356 1.25475466 0.84855868 -0.87653694 0.94681725 -0.07623013
#> [14,] -1.2330191 0.69650062 0.24364744 0.35221831 1.00517071 -0.85601738
#> [15,] -2.3214673 0.90707702 1.59345546 -0.33982651 -0.51746875 -2.19885874
#> [16,] -2.6479422 0.70541253 0.78164085 -0.12218988 0.26382110 -0.97264696
#> [17,] -0.5902429 1.20133709 0.02691916 -0.45526058 -0.38894004 -0.51744565
#> [18,] 0.7364927 0.46852245 -0.18419048 -0.45872544 -0.30400190 -0.15411996
#> [19,] -1.1850170 0.09703486 -1.47443389 -0.31309733 0.46295088 0.22911528
#> [20,] -1.4390656 0.52556741 0.11066551 -0.46711508 0.70135198 -0.12657426
#> [21,] 0.3326983 1.15647992 -0.49742370 -0.49751150 0.03075929 -0.26023128
#> [22,] -1.2838413 -0.23799459 -0.21107354 0.50469061 0.13088191 -0.02655946
#> [23,] 0.9762797 0.70172088 0.20561742 -0.60785236 -0.37714270 -0.15010150
#> [24,] -2.3558246 0.03526826 0.80988172 -0.01076392 0.79025721 -0.53162513
#> [25,] -0.5905978 -0.27969847 -0.34246431 0.49615203 0.23346261 -0.50946404
#Select the number of components in the scores matrix
predict(modpls,newdata=Xpine_sup,type="scores",comps=1)
#> Comp_1
#> [1,] -1.3617679
#> [2,] 2.7226457
#> [3,] 0.2057330
#> [4,] 1.7145547
#> [5,] 1.5468388
#> [6,] -1.0438828
#> [7,] 3.7604036
#> [8,] 2.2876515
#> [9,] 2.0411399
#> [10,] 0.1570251
#> [11,] 2.4095509
#> [12,] 0.7491762
#> [13,] -2.3485356
#> [14,] -1.2330191
#> [15,] -2.3214673
#> [16,] -2.6479422
#> [17,] -0.5902429
#> [18,] 0.7364927
#> [19,] -1.1850170
#> [20,] -1.4390656
#> [21,] 0.3326983
#> [22,] -1.2838413
#> [23,] 0.9762797
#> [24,] -2.3558246
#> [25,] -0.5905978
predict(modpls,newdata=Xpine_sup,type="scores",comps=3)
#> Comp_1 Comp_2 Comp_3
#> [1,] -1.3617679 1.84853757 -0.07167989
#> [2,] 2.7226457 -0.18222256 0.81656680
#> [3,] 0.2057330 0.81889037 1.42664508
#> [4,] 1.7145547 0.87851109 0.32284764
#> [5,] 1.5468388 0.06396562 0.85245128
#> [6,] -1.0438828 2.94178014 1.47387097
#> [7,] 3.7604036 1.20512548 -0.18065028
#> [8,] 2.2876515 0.79101480 0.59799022
#> [9,] 2.0411399 0.23491526 0.20046643
#> [10,] 0.1570251 1.48472812 0.85445008
#> [11,] 2.4095509 1.50815475 0.24927003
#> [12,] 0.7491762 1.04259904 0.38939526
#> [13,] -2.3485356 1.25475466 0.84855868
#> [14,] -1.2330191 0.69650062 0.24364744
#> [15,] -2.3214673 0.90707702 1.59345546
#> [16,] -2.6479422 0.70541253 0.78164085
#> [17,] -0.5902429 1.20133709 0.02691916
#> [18,] 0.7364927 0.46852245 -0.18419048
#> [19,] -1.1850170 0.09703486 -1.47443389
#> [20,] -1.4390656 0.52556741 0.11066551
#> [21,] 0.3326983 1.15647992 -0.49742370
#> [22,] -1.2838413 -0.23799459 -0.21107354
#> [23,] 0.9762797 0.70172088 0.20561742
#> [24,] -2.3558246 0.03526826 0.80988172
#> [25,] -0.5905978 -0.27969847 -0.34246431
predict(modpls,newdata=Xpine_sup,type="scores",comps=6)
#> Comp_1 Comp_2 Comp_3 Comp_4 Comp_5 Comp_6
#> [1,] -1.3617679 1.84853757 -0.07167989 -0.78643051 -0.55491628 -0.31285630
#> [2,] 2.7226457 -0.18222256 0.81656680 0.02721720 0.86067342 -0.37393001
#> [3,] 0.2057330 0.81889037 1.42664508 -0.15485978 0.43372368 -0.65684336
#> [4,] 1.7145547 0.87851109 0.32284764 0.37343500 0.23900014 -0.44019550
#> [5,] 1.5468388 0.06396562 0.85245128 -0.16192486 0.59815956 -0.33518539
#> [6,] -1.0438828 2.94178014 1.47387097 1.08467443 -0.02394675 0.87054622
#> [7,] 3.7604036 1.20512548 -0.18065028 -0.40642515 -0.37373716 -0.66809476
#> [8,] 2.2876515 0.79101480 0.59799022 -0.32260550 0.35381984 -0.45046964
#> [9,] 2.0411399 0.23491526 0.20046643 0.46551988 0.41574301 -0.22843097
#> [10,] 0.1570251 1.48472812 0.85445008 0.03281035 0.58589348 0.13631629
#> [11,] 2.4095509 1.50815475 0.24927003 -1.08425752 0.08663564 -0.32703134
#> [12,] 0.7491762 1.04259904 0.38939526 1.71739480 -0.33430079 0.52239057
#> [13,] -2.3485356 1.25475466 0.84855868 -0.87653694 0.94681725 -0.07623013
#> [14,] -1.2330191 0.69650062 0.24364744 0.35221831 1.00517071 -0.85601738
#> [15,] -2.3214673 0.90707702 1.59345546 -0.33982651 -0.51746875 -2.19885874
#> [16,] -2.6479422 0.70541253 0.78164085 -0.12218988 0.26382110 -0.97264696
#> [17,] -0.5902429 1.20133709 0.02691916 -0.45526058 -0.38894004 -0.51744565
#> [18,] 0.7364927 0.46852245 -0.18419048 -0.45872544 -0.30400190 -0.15411996
#> [19,] -1.1850170 0.09703486 -1.47443389 -0.31309733 0.46295088 0.22911528
#> [20,] -1.4390656 0.52556741 0.11066551 -0.46711508 0.70135198 -0.12657426
#> [21,] 0.3326983 1.15647992 -0.49742370 -0.49751150 0.03075929 -0.26023128
#> [22,] -1.2838413 -0.23799459 -0.21107354 0.50469061 0.13088191 -0.02655946
#> [23,] 0.9762797 0.70172088 0.20561742 -0.60785236 -0.37714270 -0.15010150
#> [24,] -2.3558246 0.03526826 0.80988172 -0.01076392 0.79025721 -0.53162513
#> [25,] -0.5905978 -0.27969847 -0.34246431 0.49615203 0.23346261 -0.50946404
try(predict(modpls,newdata=Xpine_sup,type="scores",comps=8))
#> Error in predict.plsRglmmodel(modpls, newdata = Xpine_sup, type = "scores", :
#> Cannot predict using more components than extracted.
#Identical to modpls2NA$ValsPredictY
predict(modpls,newdata=Xpine_supNA,type="response",methodNA="missingdata")
#> Prediction as if missing values in every row.
#> [1] 2.6822545 1.1863259 1.1158440 1.3838529 0.9095165 3.0886290
#> [7] 1.4840306 1.3932964 1.5820746 1.7099538 1.7180268 1.7661632
#> [13] 0.8530780 0.4951882 0.3444462 0.4816824 0.8115274 1.0833567
#> [19] 0.3552001 0.4309125 1.2581587 0.7244769 1.3761161 -0.0205930
#> [25] 0.1081691
cbind(predict(modpls,newdata=Xpine_supNA,type="response"),
predict(modplsform,newdata=Xpine_supNA,type="response"))
#> Missing value in row 1 .
#> Missing value in row 1 .
#> [,1] [,2]
#> [1,] 2.6822545 2.6822545
#> [2,] 1.6368638 1.6368638
#> [3,] 1.4742716 1.4742716
#> [4,] 1.7447487 1.7447487
#> [5,] 1.4156388 1.4156388
#> [6,] 2.9500255 2.9500255
#> [7,] 2.0414347 2.0414347
#> [8,] 1.8312096 1.8312096
#> [9,] 1.5718912 1.5718912
#> [10,] 2.0025140 2.0025140
#> [11,] 2.0487486 2.0487486
#> [12,] 2.0535109 2.0535109
#> [13,] 1.0628496 1.0628496
#> [14,] 0.8116580 0.8116580
#> [15,] 0.1798506 0.1798506
#> [16,] 0.3632888 0.3632888
#> [17,] 0.9930354 0.9930354
#> [18,] 1.0399233 1.0399233
#> [19,] 0.2747613 0.2747613
#> [20,] 0.7296300 0.7296300
#> [21,] 1.2241840 1.2241840
#> [22,] 0.3757749 0.3757749
#> [23,] 1.2966168 1.2966168
#> [24,] 0.3423249 0.3423249
#> [25,] 0.3425252 0.3425252
predict(modpls,newdata=Xpine_supNA,type="response",comps=1)
#> Missing value in row 1 .
#> [1] 0.7935852 1.5275250 0.8653393 1.2623019 1.2181767 0.5365724 1.8005533
#> [8] 1.4130804 1.3482246 0.8525246 1.4451515 1.0083162 0.1933256 0.4868117
#> [15] 0.2004471 0.1145533 0.6559225 1.0049792 0.4994408 0.4326020 0.8987432
#> [22] 0.4734407 1.0680659 0.1914079 0.6558292
predict(modpls,newdata=Xpine_supNA,type="response",comps=3)
#> Missing value in row 1 .
#> [1] 2.6350800 1.6412293 1.6692276 1.8131310 1.4721576 2.4824189 2.3952957
#> [8] 1.9883342 1.5249653 1.8631319 2.3120849 1.6636405 1.0800241 0.9203430
#> [15] 1.0943056 0.6916340 1.3022034 1.2067980 0.1706657 0.7408545 1.3858670
#> [22] 0.2923245 1.4945633 0.4191331 0.4186188
predict(modpls,newdata=Xpine_supNA,type="response",comps=6)
#> Missing value in row 1 .
#> [1] 2.6822545 1.6368638 1.4742716 1.7447487 1.4156388 2.9500255 2.0414347
#> [8] 1.8312096 1.5718912 2.0025140 2.0487486 2.0535109 1.0628496 0.8116580
#> [15] 0.1798506 0.3632888 0.9930354 1.0399233 0.2747613 0.7296300 1.2241840
#> [22] 0.3757749 1.2966168 0.3423249 0.3425252
try(predict(modpls,newdata=Xpine_supNA,type="response",comps=8))
#> Error in predict.plsRglmmodel(modpls, newdata = Xpine_supNA, type = "response", :
#> Cannot predict using more components than extracted.
#Identical to modpls2NA$ttPredictY
predict(modpls,newdata=Xpine_supNA,type="scores",methodNA="missingdata")
#> Prediction as if missing values in every row.
#> Comp_1 Comp_2 Comp_3 Comp_4 Comp_5 Comp_6
#> [1,] -0.06699854 3.66196285 -0.41608333 0.45187568 1.59546224 -0.70283222
#> [2,] 2.70687391 -0.58366935 0.27090780 -0.23268469 0.41783324 -0.34378821
#> [3,] 0.19202296 0.49815234 0.85544720 -0.24904846 0.19019882 -0.62164030
#> [4,] 1.70925990 0.55904242 -0.30881117 0.25656004 0.01838154 -0.37653562
#> [5,] 1.52593733 -0.38881272 0.06295091 -0.28629453 0.25482060 -0.30053699
#> [6,] -1.05490144 3.06326417 1.16226073 1.75918466 0.63562354 0.76498730
#> [7,] 3.76970759 0.71212677 -0.93264756 -1.01046463 -1.05410585 -0.43291609
#> [8,] 2.27575778 0.40145391 -0.07597958 -0.49309936 0.02646672 -0.39687347
#> [9,] 2.05501340 0.24531251 -0.02869079 0.50618778 0.53512630 -0.12610842
#> [10,] 0.14426832 1.22229503 0.30409563 0.03954890 0.46422395 0.16211651
#> [11,] 2.42011239 1.21572929 -0.52692173 -1.23349885 -0.07511353 -0.13789070
#> [12,] 0.71325904 0.78138784 -0.22320815 2.05984947 -0.23708707 0.39657611
#> [13,] -2.36496665 1.06490787 0.67009104 -0.97605714 0.71251859 -0.10284336
#> [14,] -1.24656968 0.41305228 0.07580642 0.00245363 0.50871863 -0.84205602
#> [15,] -2.30296146 1.06483113 1.46884767 0.00434439 -0.02752066 -2.23807126
#> [16,] -2.64053336 0.81365140 0.52481842 0.27545183 0.73661798 -0.97879522
#> [17,] -0.60476055 1.04011407 -0.29486241 -0.35457355 -0.40072127 -0.57840396
#> [18,] 0.72843736 0.50469321 -0.16432560 -0.30879645 -0.18587858 -0.20436368
#> [19,] -1.18710588 0.16576762 -1.16075056 -0.42137277 0.35583549 0.21512882
#> [20,] -1.45582497 0.26069143 -0.29422583 -0.49842200 0.51864437 -0.17557750
#> [21,] 0.32934702 1.18545904 -0.32589387 -0.54271388 -0.02733918 -0.28729932
#> [22,] -1.27473171 0.07149663 -0.08468023 0.98077026 0.73852299 -0.05307274
#> [23,] 0.99076234 0.77336043 0.10840383 -0.54965195 -0.22267535 -0.06219731
#> [24,] -2.37627373 -0.28604262 0.49260588 -0.18770162 0.42544962 -0.60255367
#> [25,] -0.60942177 -0.48739596 -0.69025348 0.57675181 0.16727880 -0.59655697
predict(modplsform,newdata=Xpine_supNA,type="scores",methodNA="missingdata")
#> Prediction as if missing values in every row.
#> Comp_1 Comp_2 Comp_3 Comp_4 Comp_5 Comp_6
#> [1,] -0.06699854 3.66196285 -0.41608333 0.45187568 1.59546224 -0.70283222
#> [2,] 2.70687391 -0.58366935 0.27090780 -0.23268469 0.41783324 -0.34378821
#> [3,] 0.19202296 0.49815234 0.85544720 -0.24904846 0.19019882 -0.62164030
#> [4,] 1.70925990 0.55904242 -0.30881117 0.25656004 0.01838154 -0.37653562
#> [5,] 1.52593733 -0.38881272 0.06295091 -0.28629453 0.25482060 -0.30053699
#> [6,] -1.05490144 3.06326417 1.16226073 1.75918466 0.63562354 0.76498730
#> [7,] 3.76970759 0.71212677 -0.93264756 -1.01046463 -1.05410585 -0.43291609
#> [8,] 2.27575778 0.40145391 -0.07597958 -0.49309936 0.02646672 -0.39687347
#> [9,] 2.05501340 0.24531251 -0.02869079 0.50618778 0.53512630 -0.12610842
#> [10,] 0.14426832 1.22229503 0.30409563 0.03954890 0.46422395 0.16211651
#> [11,] 2.42011239 1.21572929 -0.52692173 -1.23349885 -0.07511353 -0.13789070
#> [12,] 0.71325904 0.78138784 -0.22320815 2.05984947 -0.23708707 0.39657611
#> [13,] -2.36496665 1.06490787 0.67009104 -0.97605714 0.71251859 -0.10284336
#> [14,] -1.24656968 0.41305228 0.07580642 0.00245363 0.50871863 -0.84205602
#> [15,] -2.30296146 1.06483113 1.46884767 0.00434439 -0.02752066 -2.23807126
#> [16,] -2.64053336 0.81365140 0.52481842 0.27545183 0.73661798 -0.97879522
#> [17,] -0.60476055 1.04011407 -0.29486241 -0.35457355 -0.40072127 -0.57840396
#> [18,] 0.72843736 0.50469321 -0.16432560 -0.30879645 -0.18587858 -0.20436368
#> [19,] -1.18710588 0.16576762 -1.16075056 -0.42137277 0.35583549 0.21512882
#> [20,] -1.45582497 0.26069143 -0.29422583 -0.49842200 0.51864437 -0.17557750
#> [21,] 0.32934702 1.18545904 -0.32589387 -0.54271388 -0.02733918 -0.28729932
#> [22,] -1.27473171 0.07149663 -0.08468023 0.98077026 0.73852299 -0.05307274
#> [23,] 0.99076234 0.77336043 0.10840383 -0.54965195 -0.22267535 -0.06219731
#> [24,] -2.37627373 -0.28604262 0.49260588 -0.18770162 0.42544962 -0.60255367
#> [25,] -0.60942177 -0.48739596 -0.69025348 0.57675181 0.16727880 -0.59655697
predict(modpls,newdata=Xpine_supNA,type="scores")
#> Missing value in row 1 .
#> Comp_1 Comp_2 Comp_3 Comp_4 Comp_5 Comp_6
#> [1,] -0.06699854 3.66196285 -0.41608333 0.45187568 1.59546224 -0.70283222
#> [2,] 2.72264571 -0.18222256 0.81656680 0.02721720 0.86067342 -0.37393001
#> [3,] 0.20573296 0.81889037 1.42664508 -0.15485978 0.43372368 -0.65684336
#> [4,] 1.71455473 0.87851109 0.32284764 0.37343500 0.23900014 -0.44019550
#> [5,] 1.54683876 0.06396562 0.85245128 -0.16192486 0.59815956 -0.33518539
#> [6,] -1.04388279 2.94178014 1.47387097 1.08467443 -0.02394675 0.87054622
#> [7,] 3.76040357 1.20512548 -0.18065028 -0.40642515 -0.37373716 -0.66809476
#> [8,] 2.28765145 0.79101480 0.59799022 -0.32260550 0.35381984 -0.45046964
#> [9,] 2.04113990 0.23491526 0.20046643 0.46551988 0.41574301 -0.22843097
#> [10,] 0.15702512 1.48472812 0.85445008 0.03281035 0.58589348 0.13631629
#> [11,] 2.40955090 1.50815475 0.24927003 -1.08425752 0.08663564 -0.32703134
#> [12,] 0.74917624 1.04259904 0.38939526 1.71739480 -0.33430079 0.52239057
#> [13,] -2.34853556 1.25475466 0.84855868 -0.87653694 0.94681725 -0.07623013
#> [14,] -1.23301912 0.69650062 0.24364744 0.35221831 1.00517071 -0.85601738
#> [15,] -2.32146728 0.90707702 1.59345546 -0.33982651 -0.51746875 -2.19885874
#> [16,] -2.64794221 0.70541253 0.78164085 -0.12218988 0.26382110 -0.97264696
#> [17,] -0.59024285 1.20133709 0.02691916 -0.45526058 -0.38894004 -0.51744565
#> [18,] 0.73649265 0.46852245 -0.18419048 -0.45872544 -0.30400190 -0.15411996
#> [19,] -1.18501704 0.09703486 -1.47443389 -0.31309733 0.46295088 0.22911528
#> [20,] -1.43906556 0.52556741 0.11066551 -0.46711508 0.70135198 -0.12657426
#> [21,] 0.33269832 1.15647992 -0.49742370 -0.49751150 0.03075929 -0.26023128
#> [22,] -1.28384132 -0.23799459 -0.21107354 0.50469061 0.13088191 -0.02655946
#> [23,] 0.97627971 0.70172088 0.20561742 -0.60785236 -0.37714270 -0.15010150
#> [24,] -2.35582456 0.03526826 0.80988172 -0.01076392 0.79025721 -0.53162513
#> [25,] -0.59059777 -0.27969847 -0.34246431 0.49615203 0.23346261 -0.50946404
predict(modplsform,newdata=Xpine_supNA,type="scores")
#> Missing value in row 1 .
#> Comp_1 Comp_2 Comp_3 Comp_4 Comp_5 Comp_6
#> [1,] -0.06699854 3.66196285 -0.41608333 0.45187568 1.59546224 -0.70283222
#> [2,] 2.72264571 -0.18222256 0.81656680 0.02721720 0.86067342 -0.37393001
#> [3,] 0.20573296 0.81889037 1.42664508 -0.15485978 0.43372368 -0.65684336
#> [4,] 1.71455473 0.87851109 0.32284764 0.37343500 0.23900014 -0.44019550
#> [5,] 1.54683876 0.06396562 0.85245128 -0.16192486 0.59815956 -0.33518539
#> [6,] -1.04388279 2.94178014 1.47387097 1.08467443 -0.02394675 0.87054622
#> [7,] 3.76040357 1.20512548 -0.18065028 -0.40642515 -0.37373716 -0.66809476
#> [8,] 2.28765145 0.79101480 0.59799022 -0.32260550 0.35381984 -0.45046964
#> [9,] 2.04113990 0.23491526 0.20046643 0.46551988 0.41574301 -0.22843097
#> [10,] 0.15702512 1.48472812 0.85445008 0.03281035 0.58589348 0.13631629
#> [11,] 2.40955090 1.50815475 0.24927003 -1.08425752 0.08663564 -0.32703134
#> [12,] 0.74917624 1.04259904 0.38939526 1.71739480 -0.33430079 0.52239057
#> [13,] -2.34853556 1.25475466 0.84855868 -0.87653694 0.94681725 -0.07623013
#> [14,] -1.23301912 0.69650062 0.24364744 0.35221831 1.00517071 -0.85601738
#> [15,] -2.32146728 0.90707702 1.59345546 -0.33982651 -0.51746875 -2.19885874
#> [16,] -2.64794221 0.70541253 0.78164085 -0.12218988 0.26382110 -0.97264696
#> [17,] -0.59024285 1.20133709 0.02691916 -0.45526058 -0.38894004 -0.51744565
#> [18,] 0.73649265 0.46852245 -0.18419048 -0.45872544 -0.30400190 -0.15411996
#> [19,] -1.18501704 0.09703486 -1.47443389 -0.31309733 0.46295088 0.22911528
#> [20,] -1.43906556 0.52556741 0.11066551 -0.46711508 0.70135198 -0.12657426
#> [21,] 0.33269832 1.15647992 -0.49742370 -0.49751150 0.03075929 -0.26023128
#> [22,] -1.28384132 -0.23799459 -0.21107354 0.50469061 0.13088191 -0.02655946
#> [23,] 0.97627971 0.70172088 0.20561742 -0.60785236 -0.37714270 -0.15010150
#> [24,] -2.35582456 0.03526826 0.80988172 -0.01076392 0.79025721 -0.53162513
#> [25,] -0.59059777 -0.27969847 -0.34246431 0.49615203 0.23346261 -0.50946404
predict(modpls,newdata=Xpine_supNA,type="scores",comps=1)
#> Missing value in row 1 .
#> Comp_1
#> [1,] -0.06699854
#> [2,] 2.72264571
#> [3,] 0.20573296
#> [4,] 1.71455473
#> [5,] 1.54683876
#> [6,] -1.04388279
#> [7,] 3.76040357
#> [8,] 2.28765145
#> [9,] 2.04113990
#> [10,] 0.15702512
#> [11,] 2.40955090
#> [12,] 0.74917624
#> [13,] -2.34853556
#> [14,] -1.23301912
#> [15,] -2.32146728
#> [16,] -2.64794221
#> [17,] -0.59024285
#> [18,] 0.73649265
#> [19,] -1.18501704
#> [20,] -1.43906556
#> [21,] 0.33269832
#> [22,] -1.28384132
#> [23,] 0.97627971
#> [24,] -2.35582456
#> [25,] -0.59059777
predict(modpls,newdata=Xpine_supNA,type="scores",comps=3)
#> Missing value in row 1 .
#> Comp_1 Comp_2 Comp_3
#> [1,] -0.06699854 3.66196285 -0.41608333
#> [2,] 2.72264571 -0.18222256 0.81656680
#> [3,] 0.20573296 0.81889037 1.42664508
#> [4,] 1.71455473 0.87851109 0.32284764
#> [5,] 1.54683876 0.06396562 0.85245128
#> [6,] -1.04388279 2.94178014 1.47387097
#> [7,] 3.76040357 1.20512548 -0.18065028
#> [8,] 2.28765145 0.79101480 0.59799022
#> [9,] 2.04113990 0.23491526 0.20046643
#> [10,] 0.15702512 1.48472812 0.85445008
#> [11,] 2.40955090 1.50815475 0.24927003
#> [12,] 0.74917624 1.04259904 0.38939526
#> [13,] -2.34853556 1.25475466 0.84855868
#> [14,] -1.23301912 0.69650062 0.24364744
#> [15,] -2.32146728 0.90707702 1.59345546
#> [16,] -2.64794221 0.70541253 0.78164085
#> [17,] -0.59024285 1.20133709 0.02691916
#> [18,] 0.73649265 0.46852245 -0.18419048
#> [19,] -1.18501704 0.09703486 -1.47443389
#> [20,] -1.43906556 0.52556741 0.11066551
#> [21,] 0.33269832 1.15647992 -0.49742370
#> [22,] -1.28384132 -0.23799459 -0.21107354
#> [23,] 0.97627971 0.70172088 0.20561742
#> [24,] -2.35582456 0.03526826 0.80988172
#> [25,] -0.59059777 -0.27969847 -0.34246431
predict(modpls,newdata=Xpine_supNA,type="scores",comps=6)
#> Missing value in row 1 .
#> Comp_1 Comp_2 Comp_3 Comp_4 Comp_5 Comp_6
#> [1,] -0.06699854 3.66196285 -0.41608333 0.45187568 1.59546224 -0.70283222
#> [2,] 2.72264571 -0.18222256 0.81656680 0.02721720 0.86067342 -0.37393001
#> [3,] 0.20573296 0.81889037 1.42664508 -0.15485978 0.43372368 -0.65684336
#> [4,] 1.71455473 0.87851109 0.32284764 0.37343500 0.23900014 -0.44019550
#> [5,] 1.54683876 0.06396562 0.85245128 -0.16192486 0.59815956 -0.33518539
#> [6,] -1.04388279 2.94178014 1.47387097 1.08467443 -0.02394675 0.87054622
#> [7,] 3.76040357 1.20512548 -0.18065028 -0.40642515 -0.37373716 -0.66809476
#> [8,] 2.28765145 0.79101480 0.59799022 -0.32260550 0.35381984 -0.45046964
#> [9,] 2.04113990 0.23491526 0.20046643 0.46551988 0.41574301 -0.22843097
#> [10,] 0.15702512 1.48472812 0.85445008 0.03281035 0.58589348 0.13631629
#> [11,] 2.40955090 1.50815475 0.24927003 -1.08425752 0.08663564 -0.32703134
#> [12,] 0.74917624 1.04259904 0.38939526 1.71739480 -0.33430079 0.52239057
#> [13,] -2.34853556 1.25475466 0.84855868 -0.87653694 0.94681725 -0.07623013
#> [14,] -1.23301912 0.69650062 0.24364744 0.35221831 1.00517071 -0.85601738
#> [15,] -2.32146728 0.90707702 1.59345546 -0.33982651 -0.51746875 -2.19885874
#> [16,] -2.64794221 0.70541253 0.78164085 -0.12218988 0.26382110 -0.97264696
#> [17,] -0.59024285 1.20133709 0.02691916 -0.45526058 -0.38894004 -0.51744565
#> [18,] 0.73649265 0.46852245 -0.18419048 -0.45872544 -0.30400190 -0.15411996
#> [19,] -1.18501704 0.09703486 -1.47443389 -0.31309733 0.46295088 0.22911528
#> [20,] -1.43906556 0.52556741 0.11066551 -0.46711508 0.70135198 -0.12657426
#> [21,] 0.33269832 1.15647992 -0.49742370 -0.49751150 0.03075929 -0.26023128
#> [22,] -1.28384132 -0.23799459 -0.21107354 0.50469061 0.13088191 -0.02655946
#> [23,] 0.97627971 0.70172088 0.20561742 -0.60785236 -0.37714270 -0.15010150
#> [24,] -2.35582456 0.03526826 0.80988172 -0.01076392 0.79025721 -0.53162513
#> [25,] -0.59059777 -0.27969847 -0.34246431 0.49615203 0.23346261 -0.50946404
try(predict(modpls,newdata=Xpine_supNA,type="scores",comps=8))
#> Error in predict.plsRglmmodel(modpls, newdata = Xpine_supNA, type = "scores", :
#> Cannot predict using more components than extracted.
# }