This function provides a predict method for the class "plsRcoxmodel"

# S3 method for plsRcoxmodel
predict(
  object,
  newdata,
  comps = object$computed_nt,
  type = c("lp", "risk", "expected", "terms", "scores"),
  se.fit = FALSE,
  weights,
  methodNA = "adaptative",
  verbose = TRUE,
  ...
)

Arguments

object

An object of the class "plsRcoxmodel".

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. Choices are the linear predictor ("lp"), the risk score exp(lp) ("risk"), the expected number of events given the covariates and follow-up time ("expected"), the terms of the linear predictor ("terms") 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 weights is a vector of integers, then the estimated coefficients are equivalent to estimating the model from data with the individual cases replicated as many times as indicated by weights.

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 some details be displayed ?

...

Arguments to be passed on to survival::coxph and to plsRglm::PLS_lm.

Value

When type is "response", a matrix of predicted response values is returned.
When type is "scores", a score matrix is returned.

References

plsRcox, Cox-Models in a high dimensional setting in R, Frederic Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014). Proceedings of User2014!, Los Angeles, page 152.

Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404, doi:10.1093/bioinformatics/btu660.

See also

Author

Frédéric Bertrand
frederic.bertrand@math.unistra.fr
http://www-irma.u-strasbg.fr/~fbertran/

Examples

data(micro.censure) data(Xmicro.censure_compl_imp) X_train_micro <- apply((as.matrix(Xmicro.censure_compl_imp)),FUN="as.numeric",MARGIN=2)[1:80,] Y_train_micro <- micro.censure$survyear[1:80] C_train_micro <- micro.censure$DC[1:80] modpls <- plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=3)
#> ____************************************************____ #> ____Component____ 1 ____ #> ____Component____ 2 ____ #> ____Component____ 3 ____ #> ____Predicting X without NA neither in X nor in Y____ #> ****________________________________________________**** #>
predict(modpls)
#> 1 2 3 4 5 6 #> -3.91149897 -0.01103427 2.29488351 0.77390739 1.81792717 -2.07323031 #> 7 8 9 10 11 12 #> -2.31730512 4.07156130 -1.81103896 1.32578127 2.48317798 -1.91438957 #> 13 14 15 16 17 18 #> 0.69486087 -1.40225489 -0.70950316 -0.24670449 -3.67870398 -4.16645669 #> 19 20 21 22 23 24 #> 1.81768091 -3.02734320 -2.13745459 -1.92831286 0.98589347 1.14971022 #> 25 26 27 28 29 30 #> -5.89616340 -2.34994661 4.42353417 0.80061019 -0.51396920 -3.08443396 #> 31 32 33 34 35 36 #> 3.56453166 -3.55117411 -2.73950599 0.22916899 2.63357746 0.08206085 #> 37 38 39 40 41 42 #> 5.74551756 -0.15211159 -3.34205848 0.50932362 1.49286943 1.00697968 #> 43 44 45 46 47 48 #> -1.69053571 -0.43807672 -1.88100209 0.57105812 -1.94086294 0.13737583 #> 49 50 51 52 53 54 #> 2.91478046 -0.19794750 -0.53317032 1.81309897 0.05410920 6.74653169 #> 55 56 57 58 59 60 #> 6.02372222 -3.15552724 -5.38533967 1.93977635 0.36991240 -1.30359496 #> 61 62 63 64 65 66 #> 3.40800015 2.42212390 6.44149568 -1.36663839 3.82119344 0.41050691 #> 67 68 69 70 71 72 #> -5.59789050 -1.73207956 3.64545451 -2.33472431 1.90409918 2.74143690 #> 73 74 75 76 77 78 #> 2.27208915 -1.50499888 1.61558478 2.36442373 -1.52350702 -1.62287584 #> 79 80 #> -1.11760516 -5.22936005
#Identical to predict(modpls,type="lp") predict(modpls,type="risk")
#> 1 2 3 4 5 6 #> 2.001048e-02 9.890264e-01 9.923280e+00 2.168222e+00 6.159078e+00 1.257788e-01 #> 7 8 9 10 11 12 #> 9.853878e-02 5.864846e+01 1.634842e-01 3.765126e+00 1.197927e+01 1.474318e-01 #> 13 14 15 16 17 18 #> 2.003430e+00 2.460415e-01 4.918885e-01 7.813716e-01 2.525569e-02 1.550711e-02 #> 19 20 21 22 23 24 #> 6.157562e+00 4.844417e-02 1.179547e-01 1.453933e-01 2.680205e+00 3.157278e+00 #> 25 26 27 28 29 30 #> 2.749975e-03 9.537425e-02 8.339048e+01 2.226899e+00 5.981168e-01 4.575593e-02 #> 31 32 33 34 35 36 #> 3.532291e+01 2.869093e-02 6.460225e-02 1.257555e+00 1.392349e+01 1.085522e+00 #> 37 38 39 40 41 42 #> 3.127855e+02 8.588924e-01 3.536409e-02 1.664165e+00 4.449846e+00 2.737321e+00 #> 43 44 45 46 47 48 #> 1.844207e-01 6.452763e-01 1.524373e-01 1.770139e+00 1.435800e-01 1.147259e+00 #> 49 50 51 52 53 54 #> 1.844476e+01 8.204129e-01 5.867419e-01 6.129413e+00 1.055600e+00 8.511017e+02 #> 55 56 57 58 59 60 #> 4.131134e+02 4.261593e-02 4.583283e-03 6.957195e+00 1.447608e+00 2.715538e-01 #> 61 62 63 64 65 66 #> 3.020478e+01 1.126977e+01 6.273444e+02 2.549626e-01 4.565867e+01 1.507582e+00 #> 67 68 69 70 71 72 #> 3.705673e-03 1.769161e-01 3.830018e+01 9.683718e-02 6.713357e+00 1.550925e+01 #> 73 74 75 76 77 78 #> 9.699644e+00 2.220175e-01 5.030829e+00 1.063791e+01 2.179462e-01 1.973304e-01 #> 79 80 #> 3.270621e-01 5.356952e-03
predict(modpls,type="expected")
#> [1] 2.883280e-02 1.380608e-02 8.122308e-03 3.764026e-03 7.153829e-01 #> [6] 3.147782e-04 2.466064e-04 4.834946e-01 2.282118e-03 1.405932e-03 #> [11] 1.524722e-02 1.206745e-04 5.013850e-03 6.175850e-02 1.836756e-04 #> [16] 2.917712e-04 3.525507e-04 3.880860e-05 7.152067e-01 1.215990e-02 #> [21] 4.772813e-04 1.850566e-04 6.707570e-03 3.667208e-01 6.902670e-04 #> [26] 3.561361e-05 3.374238e-01 9.010726e-03 1.496867e-03 3.745170e-05 #> [31] 2.891217e-02 2.365264e-04 9.017997e-04 4.542828e-02 2.807208e-01 #> [36] 1.260844e-01 1.265627e+00 1.198950e-02 1.320527e-05 6.011681e-02 #> [41] 1.661613e-03 2.256632e-02 3.201537e-04 7.494946e-02 5.692145e-05 #> [46] 1.501472e-01 3.603979e-02 4.642167e-03 4.616046e-02 1.182121e+00 #> [51] 2.672665e-02 1.538534e+00 1.832518e-03 1.477510e+00 1.542603e-01 #> [56] 5.948868e-04 1.150443e-03 2.597879e-03 1.193400e-02 3.154123e-02 #> [61] 3.844464e-02 1.573176e-01 1.570013e+00 6.380777e-04 2.079795e+00 #> [66] 5.629446e-04 1.499430e-05 1.458487e-03 1.032203e+00 1.124773e-02 #> [71] 7.797629e-01 9.656531e-01 3.924777e-02 1.830300e-03 1.259032e-02 #> [76] 9.023316e-01 2.531466e-02 7.984600e-04 4.565545e-03 2.167589e-05
predict(modpls,type="terms")
#> tt.1 tt.2 tt.3 #> 1 -2.18710337 -1.48886332 -0.235532283 #> 2 -0.81612842 -0.15285272 0.957946868 #> 3 1.98230440 0.21817877 0.094400341 #> 4 -0.03763442 0.71295698 0.098584833 #> 5 2.76422524 -0.99036777 0.044069693 #> 6 -0.67244092 -1.10346519 -0.297324191 #> 7 -2.58500221 0.75832999 -0.490632893 #> 8 4.44407584 0.24616686 -0.618681405 #> 9 -0.22541827 -0.58024569 -1.005375007 #> 10 -0.84227497 0.28025877 1.887797465 #> 11 2.04910586 0.79213735 -0.358065231 #> 12 -2.69538483 0.85828666 -0.077291400 #> 13 1.92380082 -1.33725629 0.108316345 #> 14 -1.86302758 0.61053657 -0.149763886 #> 15 0.64929894 -0.66908891 -0.689713189 #> 16 1.81629288 -2.07233767 0.009340293 #> 17 -2.48051171 -0.85356060 -0.344631671 #> 18 -2.62201033 -1.05144670 -0.492999659 #> 19 1.09917459 1.95539108 -1.236884761 #> 20 -2.72786696 0.70395989 -1.003436130 #> 21 -2.31982425 -1.16242677 1.344796432 #> 22 -0.33710697 -1.77249772 0.181291831 #> 23 0.83670151 -0.49844873 0.647640690 #> 24 0.92709769 0.37666249 -0.154049958 #> 25 -4.17839003 -1.00436509 -0.713408282 #> 26 -3.23849773 0.82991867 0.058632456 #> 27 3.76915233 0.10875940 0.545622446 #> 28 2.77597103 -1.04890130 -0.926459549 #> 29 -1.62093845 1.14441654 -0.037447280 #> 30 -3.52772907 0.02533532 0.417959794 #> 31 2.05389237 0.24889469 1.261744606 #> 32 -4.12827545 -0.66279231 1.239893653 #> 33 -2.77012808 -0.46454837 0.495170455 #> 34 0.32516821 1.28520067 -1.381199884 #> 35 3.50256781 -0.94459787 0.075607524 #> 36 -0.07995553 0.65671168 -0.494695292 #> 37 3.03853777 1.97459796 0.732381837 #> 38 -1.45636298 0.93856907 0.365682320 #> 39 -3.53282556 -0.26343210 0.454199186 #> 40 0.74036729 -0.64349416 0.412450488 #> 41 -0.86913640 1.77284506 0.589160765 #> 42 2.68507617 -1.49011174 -0.187984755 #> 43 -3.60834238 0.57507560 1.342731063 #> 44 -1.23093622 -0.58200803 1.374867531 #> 45 -1.59763012 0.84059090 -1.123962874 #> 46 1.10000467 -0.82419326 0.295246713 #> 47 -0.50059170 -0.96816688 -0.472104369 #> 48 1.24805046 -1.02426486 -0.086409764 #> 49 2.96084678 -1.01995965 0.973893338 #> 50 0.06882002 0.22611338 -0.492880907 #> 51 -1.11588680 0.98052575 -0.397809267 #> 52 4.01703405 -2.83545722 0.631522149 #> 53 0.90902159 -0.48356886 -0.371343527 #> 54 2.13579403 2.45286756 2.157870098 #> 55 3.74121422 1.27919360 1.003314392 #> 56 -2.77980196 -0.43857585 0.062850575 #> 57 -4.24248067 -0.52612401 -0.616734987 #> 58 -0.68204492 1.66466779 0.957153476 #> 59 -2.32344267 2.10728040 0.586074673 #> 60 -1.79884966 0.91229467 -0.417039976 #> 61 2.23759637 1.86135124 -0.690947459 #> 62 1.65814317 -0.08633734 0.850318072 #> 63 6.58084023 0.49206731 -0.631411857 #> 64 1.53997918 -2.73887126 -0.167746309 #> 65 3.35162942 0.65176772 -0.182203701 #> 66 -0.02929053 0.92472060 -0.484923160 #> 67 -3.19754704 -1.17547958 -1.224863879 #> 68 0.18287169 -0.71576532 -1.199185933 #> 69 2.37682745 1.57609851 -0.307471453 #> 70 -1.24791377 -0.41580352 -0.671007023 #> 71 -0.48138068 0.95498256 1.430497301 #> 72 2.02397146 2.38359745 -1.666132008 #> 73 2.40114203 -0.50630766 0.377254774 #> 74 -1.80842119 0.21331778 0.090104528 #> 75 2.86058583 -0.78279774 -0.462203310 #> 76 3.06726572 -0.79914281 0.096300823 #> 77 -1.26148410 0.15994260 -0.421965529 #> 78 -0.80170514 -0.09059412 -0.730576581 #> 79 -1.37219178 1.09425077 -0.839664154 #> 80 -3.95053329 -1.58030167 0.301474909 #> attr(,"constant") #> [1] -3.218013e-16
predict(modpls,type="scores")
#> Comp_1 Comp_2 Comp_3 #> 1 -1.51471639 -1.61173906 -0.44174968 #> 2 -0.56522390 -0.16546763 1.79666550 #> 3 1.37287931 0.23618504 0.17705140 #> 4 -0.02606437 0.77179724 0.18489957 #> 5 1.91441216 -1.07210272 0.08265437 #> 6 -0.46571063 -1.19453407 -0.55764274 #> 7 -1.79028814 0.82091488 -0.92020050 #> 8 3.07782184 0.26648299 -1.16036032 #> 9 -0.15611733 -0.62813331 -1.88561876 #> 10 -0.58333215 0.30338850 3.54063538 #> 11 1.41914382 0.85751237 -0.67156485 #> 12 -1.86673554 0.92912097 -0.14496294 #> 13 1.33236164 -1.44761991 0.20315139 #> 14 -1.29027208 0.66092409 -0.28088782 #> 15 0.44968325 -0.72430875 -1.29358311 #> 16 1.25790515 -2.24336748 0.01751807 #> 17 -1.71792143 -0.92400487 -0.64636970 #> 18 -1.81591875 -1.13822249 -0.92463946 #> 19 0.76125243 2.11676931 -2.31982404 #> 20 -1.88923160 0.76205763 -1.88198233 #> 21 -1.60663454 -1.25836174 2.52221645 #> 22 -0.23346928 -1.91878178 0.34001967 #> 23 0.57947215 -0.53958566 1.21467455 #> 24 0.64207759 0.40774841 -0.28892651 #> 25 -2.89381652 -1.08725524 -1.33802415 #> 26 -2.24287780 0.89841177 0.10996738 #> 27 2.61039185 0.11773530 1.02333549 #> 28 1.92254691 -1.13546701 -1.73760984 #> 29 -1.12260905 1.23886512 -0.07023379 #> 30 -2.44318998 0.02742624 0.78389936 #> 31 1.42245880 0.26943594 2.36644962 #> 32 -2.85910880 -0.71749249 2.32546733 #> 33 -1.91850027 -0.50288750 0.92871087 #> 34 0.22520089 1.39126815 -2.59049250 #> 35 2.42576411 -1.02255543 0.14180476 #> 36 -0.05537459 0.71091002 -0.92781969 #> 37 2.10439206 2.13756133 1.37360977 #> 38 -1.00862945 1.01602908 0.68585099 #> 39 -2.44671964 -0.28517313 0.85186771 #> 40 0.51275421 -0.69660167 0.77356645 #> 41 -0.60193549 1.91915778 1.10499325 #> 42 1.85959610 -1.61309051 -0.35257250 #> 43 -2.49902013 0.62253653 2.51834278 #> 44 -0.85250624 -0.63004109 2.57861593 #> 45 -1.10646646 0.90996478 -2.10803478 #> 46 0.76182732 -0.89221385 0.55374635 #> 47 -0.34669346 -1.04806959 -0.88544956 #> 48 0.86435900 -1.10879734 -0.16206477 #> 49 2.05058581 -1.10413682 1.82657371 #> 50 0.04766250 0.24477450 -0.92441674 #> 51 -0.77282677 1.06144844 -0.74610629 #> 52 2.78206663 -3.06946722 1.18444362 #> 53 0.62955868 -0.52347775 -0.69646880 #> 54 1.47918121 2.65530247 4.04716679 #> 55 2.59104283 1.38476532 1.88175400 #> 56 -1.92520009 -0.47477147 0.11787862 #> 57 -2.93820361 -0.56954497 -1.15670974 #> 58 -0.47236205 1.80205266 1.79517746 #> 59 -1.60914054 2.28119403 1.09920516 #> 60 -1.24582455 0.98758626 -0.78217421 #> 61 1.54968619 2.01496837 -1.29589803 #> 62 1.14837582 -0.09346275 1.59480363 #> 63 4.55767509 0.53267757 -1.18423676 #> 64 1.06653930 -2.96491002 -0.31461453 #> 65 2.32122911 0.70555804 -0.34172992 #> 66 -0.02028567 1.00103770 -0.90949169 #> 67 -2.21451669 -1.27249179 -2.29727842 #> 68 0.12665096 -0.77483736 -2.24911847 #> 69 1.64611310 1.70617376 -0.57667431 #> 70 -0.86426433 -0.45011974 -1.25849899 #> 71 -0.33338854 1.03379717 2.68295167 #> 72 1.40173655 2.58031550 -3.12489345 #> 73 1.66295258 -0.54809318 0.70755556 #> 74 -1.25245347 0.23092288 0.16899444 #> 75 1.98114835 -0.84740196 -0.86687975 #> 76 2.12428810 -0.86509599 0.18061583 #> 77 -0.87366270 0.17314265 -0.79141227 #> 78 -0.55523481 -0.09807084 -1.37022395 #> 79 -0.95033523 1.18455918 -1.57482181 #> 80 -2.73601038 -1.71072380 0.56542757
predict(modpls,se.fit=TRUE)
#> $fit #> 1 2 3 4 5 6 #> -3.91149897 -0.01103427 2.29488351 0.77390739 1.81792717 -2.07323031 #> 7 8 9 10 11 12 #> -2.31730512 4.07156130 -1.81103896 1.32578127 2.48317798 -1.91438957 #> 13 14 15 16 17 18 #> 0.69486087 -1.40225489 -0.70950316 -0.24670449 -3.67870398 -4.16645669 #> 19 20 21 22 23 24 #> 1.81768091 -3.02734320 -2.13745459 -1.92831286 0.98589347 1.14971022 #> 25 26 27 28 29 30 #> -5.89616340 -2.34994661 4.42353417 0.80061019 -0.51396920 -3.08443396 #> 31 32 33 34 35 36 #> 3.56453166 -3.55117411 -2.73950599 0.22916899 2.63357746 0.08206085 #> 37 38 39 40 41 42 #> 5.74551756 -0.15211159 -3.34205848 0.50932362 1.49286943 1.00697968 #> 43 44 45 46 47 48 #> -1.69053571 -0.43807672 -1.88100209 0.57105812 -1.94086294 0.13737583 #> 49 50 51 52 53 54 #> 2.91478046 -0.19794750 -0.53317032 1.81309897 0.05410920 6.74653169 #> 55 56 57 58 59 60 #> 6.02372222 -3.15552724 -5.38533967 1.93977635 0.36991240 -1.30359496 #> 61 62 63 64 65 66 #> 3.40800015 2.42212390 6.44149568 -1.36663839 3.82119344 0.41050691 #> 67 68 69 70 71 72 #> -5.59789050 -1.73207956 3.64545451 -2.33472431 1.90409918 2.74143690 #> 73 74 75 76 77 78 #> 2.27208915 -1.50499888 1.61558478 2.36442373 -1.52350702 -1.62287584 #> 79 80 #> -1.11760516 -5.22936005 #> #> $se.fit #> 1 2 3 4 5 6 7 8 #> 0.8162219 0.2763778 0.4841867 0.1945131 0.4939491 0.4477485 0.5608206 0.9168810 #> 9 10 11 12 13 14 15 16 #> 0.4488860 0.5799729 0.5418386 0.4943679 0.3531245 0.3569619 0.2650980 0.4302105 #> 17 18 19 20 21 22 23 24 #> 0.7619582 0.8630618 0.6109658 0.7205740 0.6316150 0.4856943 0.2938761 0.2498695 #> 25 26 27 28 29 30 31 32 #> 1.2262466 0.5876082 0.9420914 0.4544296 0.2932452 0.7015508 0.7918338 0.8726003 #> 33 34 35 36 37 38 39 40 #> 0.6132601 0.4553099 0.6526979 0.1856844 1.1957187 0.2515113 0.7444075 0.2118061 #> 41 42 43 44 45 46 47 48 #> 0.4586467 0.4458471 0.6223899 0.4201203 0.5349032 0.2406794 0.4248530 0.2337450 #> 49 50 51 52 53 54 55 56 #> 0.7266590 0.1480826 0.2696072 0.7941012 0.1632820 1.4785420 1.2541028 0.6686363 #> 57 58 59 60 61 62 63 64 #> 1.1303158 0.5371830 0.4846529 0.3809646 0.7815128 0.5475350 1.4139451 0.5831624 #> 65 66 67 68 69 70 71 72 #> 0.8122094 0.2311461 1.1765103 0.4766329 0.7851201 0.5009383 0.5542336 0.8408220 #> 73 74 75 76 77 78 79 80 #> 0.5247789 0.3476377 0.4720976 0.5791829 0.3427235 0.3778105 0.4108927 1.1068457 #>
#Identical to predict(modpls,type="lp") predict(modpls,type="risk",se.fit=TRUE)
#> $fit #> 1 2 3 4 5 6 #> 2.001048e-02 9.890264e-01 9.923280e+00 2.168222e+00 6.159078e+00 1.257788e-01 #> 7 8 9 10 11 12 #> 9.853878e-02 5.864846e+01 1.634842e-01 3.765126e+00 1.197927e+01 1.474318e-01 #> 13 14 15 16 17 18 #> 2.003430e+00 2.460415e-01 4.918885e-01 7.813716e-01 2.525569e-02 1.550711e-02 #> 19 20 21 22 23 24 #> 6.157562e+00 4.844417e-02 1.179547e-01 1.453933e-01 2.680205e+00 3.157278e+00 #> 25 26 27 28 29 30 #> 2.749975e-03 9.537425e-02 8.339048e+01 2.226899e+00 5.981168e-01 4.575593e-02 #> 31 32 33 34 35 36 #> 3.532291e+01 2.869093e-02 6.460225e-02 1.257555e+00 1.392349e+01 1.085522e+00 #> 37 38 39 40 41 42 #> 3.127855e+02 8.588924e-01 3.536409e-02 1.664165e+00 4.449846e+00 2.737321e+00 #> 43 44 45 46 47 48 #> 1.844207e-01 6.452763e-01 1.524373e-01 1.770139e+00 1.435800e-01 1.147259e+00 #> 49 50 51 52 53 54 #> 1.844476e+01 8.204129e-01 5.867419e-01 6.129413e+00 1.055600e+00 8.511017e+02 #> 55 56 57 58 59 60 #> 4.131134e+02 4.261593e-02 4.583283e-03 6.957195e+00 1.447608e+00 2.715538e-01 #> 61 62 63 64 65 66 #> 3.020478e+01 1.126977e+01 6.273444e+02 2.549626e-01 4.565867e+01 1.507582e+00 #> 67 68 69 70 71 72 #> 3.705673e-03 1.769161e-01 3.830018e+01 9.683718e-02 6.713357e+00 1.550925e+01 #> 73 74 75 76 77 78 #> 9.699644e+00 2.220175e-01 5.030829e+00 1.063791e+01 2.179462e-01 1.973304e-01 #> 79 80 #> 3.270621e-01 5.356952e-03 #> #> $se.fit #> 1 2 3 4 5 6 #> 0.11546146 0.27485715 1.52524793 0.28641823 1.22585771 0.15879541 #> 7 8 9 10 11 12 #> 0.17604657 7.02168369 0.18149889 1.12537539 1.87536232 0.18982169 #> 13 14 15 16 17 18 #> 0.49982148 0.17706227 0.18592584 0.38028552 0.12109069 0.10747497 #> 19 20 21 22 23 24 #> 1.51607717 0.15859861 0.21692523 0.18519753 0.48111434 0.44398632 #> 25 26 27 28 29 30 #> 0.06430462 0.18146940 8.60302677 0.67813619 0.22678997 0.15006617 #> 31 32 33 34 35 36 #> 4.70611196 0.14780451 0.15587215 0.51058791 2.43548975 0.19346158 #> 37 38 39 40 41 42 #> 21.14717201 0.23309178 0.13998837 0.27323526 0.96749953 0.73764731 #> 43 44 45 46 47 48 #> 0.26728056 0.33747878 0.20884343 0.32021565 0.16098511 0.25036470 #> 49 50 51 52 53 54 #> 3.12080913 0.13412824 0.20651660 1.96600797 0.16775979 43.13446322 #> 55 56 57 58 59 60 #> 25.48988026 0.13803078 0.07652232 1.41690048 0.58311772 0.19852377 #> 61 62 63 64 65 66 #> 4.29510626 1.83810102 35.41486238 0.29446098 5.48819583 0.28380960 #> 67 68 69 70 71 72 #> 0.07161916 0.20047844 4.85888346 0.15588535 1.43602756 3.31130759 #> 73 74 75 76 77 78 #> 1.63438469 0.16380247 1.05889179 1.88905148 0.15999948 0.16783053 #> 79 80 #> 0.23498683 0.08101137 #>
predict(modpls,type="expected",se.fit=TRUE)
#> $fit #> [1] 2.883280e-02 1.380608e-02 8.122308e-03 3.764026e-03 7.153829e-01 #> [6] 3.147782e-04 2.466064e-04 4.834946e-01 2.282118e-03 1.405932e-03 #> [11] 1.524722e-02 1.206745e-04 5.013850e-03 6.175850e-02 1.836756e-04 #> [16] 2.917712e-04 3.525507e-04 3.880860e-05 7.152067e-01 1.215990e-02 #> [21] 4.772813e-04 1.850566e-04 6.707570e-03 3.667208e-01 6.902670e-04 #> [26] 3.561361e-05 3.374238e-01 9.010726e-03 1.496867e-03 3.745170e-05 #> [31] 2.891217e-02 2.365264e-04 9.017997e-04 4.542828e-02 2.807208e-01 #> [36] 1.260844e-01 1.265627e+00 1.198950e-02 1.320527e-05 6.011681e-02 #> [41] 1.661613e-03 2.256632e-02 3.201537e-04 7.494946e-02 5.692145e-05 #> [46] 1.501472e-01 3.603979e-02 4.642167e-03 4.616046e-02 1.182121e+00 #> [51] 2.672665e-02 1.538534e+00 1.832518e-03 1.477510e+00 1.542603e-01 #> [56] 5.948868e-04 1.150443e-03 2.597879e-03 1.193400e-02 3.154123e-02 #> [61] 3.844464e-02 1.573176e-01 1.570013e+00 6.380777e-04 2.079795e+00 #> [66] 5.629446e-04 1.499430e-05 1.458487e-03 1.032203e+00 1.124773e-02 #> [71] 7.797629e-01 9.656531e-01 3.924777e-02 1.830300e-03 1.259032e-02 #> [76] 9.023316e-01 2.531466e-02 7.984600e-04 4.565545e-03 2.167589e-05 #> #> $se.fit #> [1] 3.406705e-02 1.373203e-02 8.318021e-03 4.414805e-03 3.323965e-01 #> [6] 5.283104e-04 4.339381e-04 2.960415e-01 2.844664e-03 2.018374e-03 #> [11] 1.421937e-02 2.144790e-04 5.855288e-03 6.315959e-02 3.126687e-04 #> [16] 4.834845e-04 5.653308e-04 8.141610e-05 4.461899e-01 1.605200e-02 #> [21] 7.969296e-04 3.192707e-04 7.374734e-03 1.680227e-01 1.201299e-03 #> [26] 7.095361e-05 1.822379e-01 9.960405e-03 2.109728e-03 7.457524e-05 #> [31] 2.537306e-02 4.225683e-04 1.316041e-03 3.711109e-02 1.651624e-01 #> [36] 7.893326e-02 6.531176e-01 1.205310e-02 2.846891e-05 4.379305e-02 #> [41] 2.297955e-03 2.067452e-02 5.466169e-04 6.211512e-02 1.098280e-04 #> [46] 8.881650e-02 3.543230e-02 5.465062e-03 3.813097e-02 9.892654e-01 #> [51] 2.356531e-02 1.040102e+00 2.412576e-03 1.021390e+00 1.547032e-01 #> [56] 9.035359e-04 1.917129e-03 3.440573e-03 1.339237e-02 2.815578e-02 #> [61] 3.315152e-02 1.023384e-01 1.012679e+00 1.022091e-03 8.780925e-01 #> [66] 8.589894e-04 3.456508e-05 1.978790e-03 4.882389e-01 1.153031e-02 #> [71] 4.938505e-01 6.351421e-01 3.160159e-02 2.423648e-03 1.265539e-02 #> [76] 4.133363e-01 2.244496e-02 1.206846e-03 5.308087e-03 4.848122e-05 #>
predict(modpls,type="terms",se.fit=TRUE)
#> $fit #> tt.1 tt.2 tt.3 #> 1 -2.18710337 -1.48886332 -0.235532283 #> 2 -0.81612842 -0.15285272 0.957946868 #> 3 1.98230440 0.21817877 0.094400341 #> 4 -0.03763442 0.71295698 0.098584833 #> 5 2.76422524 -0.99036777 0.044069693 #> 6 -0.67244092 -1.10346519 -0.297324191 #> 7 -2.58500221 0.75832999 -0.490632893 #> 8 4.44407584 0.24616686 -0.618681405 #> 9 -0.22541827 -0.58024569 -1.005375007 #> 10 -0.84227497 0.28025877 1.887797465 #> 11 2.04910586 0.79213735 -0.358065231 #> 12 -2.69538483 0.85828666 -0.077291400 #> 13 1.92380082 -1.33725629 0.108316345 #> 14 -1.86302758 0.61053657 -0.149763886 #> 15 0.64929894 -0.66908891 -0.689713189 #> 16 1.81629288 -2.07233767 0.009340293 #> 17 -2.48051171 -0.85356060 -0.344631671 #> 18 -2.62201033 -1.05144670 -0.492999659 #> 19 1.09917459 1.95539108 -1.236884761 #> 20 -2.72786696 0.70395989 -1.003436130 #> 21 -2.31982425 -1.16242677 1.344796432 #> 22 -0.33710697 -1.77249772 0.181291831 #> 23 0.83670151 -0.49844873 0.647640690 #> 24 0.92709769 0.37666249 -0.154049958 #> 25 -4.17839003 -1.00436509 -0.713408282 #> 26 -3.23849773 0.82991867 0.058632456 #> 27 3.76915233 0.10875940 0.545622446 #> 28 2.77597103 -1.04890130 -0.926459549 #> 29 -1.62093845 1.14441654 -0.037447280 #> 30 -3.52772907 0.02533532 0.417959794 #> 31 2.05389237 0.24889469 1.261744606 #> 32 -4.12827545 -0.66279231 1.239893653 #> 33 -2.77012808 -0.46454837 0.495170455 #> 34 0.32516821 1.28520067 -1.381199884 #> 35 3.50256781 -0.94459787 0.075607524 #> 36 -0.07995553 0.65671168 -0.494695292 #> 37 3.03853777 1.97459796 0.732381837 #> 38 -1.45636298 0.93856907 0.365682320 #> 39 -3.53282556 -0.26343210 0.454199186 #> 40 0.74036729 -0.64349416 0.412450488 #> 41 -0.86913640 1.77284506 0.589160765 #> 42 2.68507617 -1.49011174 -0.187984755 #> 43 -3.60834238 0.57507560 1.342731063 #> 44 -1.23093622 -0.58200803 1.374867531 #> 45 -1.59763012 0.84059090 -1.123962874 #> 46 1.10000467 -0.82419326 0.295246713 #> 47 -0.50059170 -0.96816688 -0.472104369 #> 48 1.24805046 -1.02426486 -0.086409764 #> 49 2.96084678 -1.01995965 0.973893338 #> 50 0.06882002 0.22611338 -0.492880907 #> 51 -1.11588680 0.98052575 -0.397809267 #> 52 4.01703405 -2.83545722 0.631522149 #> 53 0.90902159 -0.48356886 -0.371343527 #> 54 2.13579403 2.45286756 2.157870098 #> 55 3.74121422 1.27919360 1.003314392 #> 56 -2.77980196 -0.43857585 0.062850575 #> 57 -4.24248067 -0.52612401 -0.616734987 #> 58 -0.68204492 1.66466779 0.957153476 #> 59 -2.32344267 2.10728040 0.586074673 #> 60 -1.79884966 0.91229467 -0.417039976 #> 61 2.23759637 1.86135124 -0.690947459 #> 62 1.65814317 -0.08633734 0.850318072 #> 63 6.58084023 0.49206731 -0.631411857 #> 64 1.53997918 -2.73887126 -0.167746309 #> 65 3.35162942 0.65176772 -0.182203701 #> 66 -0.02929053 0.92472060 -0.484923160 #> 67 -3.19754704 -1.17547958 -1.224863879 #> 68 0.18287169 -0.71576532 -1.199185933 #> 69 2.37682745 1.57609851 -0.307471453 #> 70 -1.24791377 -0.41580352 -0.671007023 #> 71 -0.48138068 0.95498256 1.430497301 #> 72 2.02397146 2.38359745 -1.666132008 #> 73 2.40114203 -0.50630766 0.377254774 #> 74 -1.80842119 0.21331778 0.090104528 #> 75 2.86058583 -0.78279774 -0.462203310 #> 76 3.06726572 -0.79914281 0.096300823 #> 77 -1.26148410 0.15994260 -0.421965529 #> 78 -0.80170514 -0.09059412 -0.730576581 #> 79 -1.37219178 1.09425077 -0.839664154 #> 80 -3.95053329 -1.58030167 0.301474909 #> attr(,"constant") #> [1] -3.218013e-16 #> #> $se.fit #> tt.1 tt.2 tt.3 #> 1 0.474934889 0.386146562 0.078545808 #> 2 0.177224300 0.039643364 0.319458164 #> 3 0.430462288 0.056586108 0.031480827 #> 4 0.008172408 0.184910113 0.032876280 #> 5 0.600258328 0.256858439 0.014696455 #> 6 0.146022205 0.286191005 0.099152305 #> 7 0.561339605 0.196677904 0.163617303 #> 8 0.965042028 0.063845006 0.206319194 #> 9 0.048950133 0.150490562 0.335274601 #> 10 0.182902087 0.072686969 0.629546723 #> 11 0.444968391 0.205446068 0.119408357 #> 12 0.585309462 0.222602330 0.025775301 #> 13 0.417758091 0.346826273 0.036121566 #> 14 0.404561032 0.158346702 0.049943580 #> 15 0.140996866 0.173532640 0.230007025 #> 16 0.394412477 0.537474496 0.003114821 #> 17 0.538649234 0.221376593 0.114928504 #> 18 0.569376009 0.272699663 0.164406577 #> 19 0.238688472 0.507143624 0.412478967 #> 20 0.592363036 0.182576660 0.334628020 #> 21 0.503755555 0.301483081 0.448465581 #> 22 0.073203610 0.459709022 0.060457586 #> 23 0.181691797 0.129275980 0.215976598 #> 24 0.201321549 0.097689911 0.051372908 #> 25 0.907347699 0.260488736 0.237908915 #> 26 0.703247767 0.215244902 0.019552876 #> 27 0.818480723 0.028207470 0.181955336 #> 28 0.602808955 0.272039497 0.308957706 #> 29 0.351990782 0.296812007 0.012487999 #> 30 0.766055066 0.006570882 0.139382123 #> 31 0.446007792 0.064552485 0.420769281 #> 32 0.896465194 0.171899574 0.413482379 #> 33 0.601540143 0.120483695 0.165130499 #> 34 0.070611078 0.333325303 0.460605482 #> 35 0.760591236 0.244987711 0.025213758 #> 36 0.017362541 0.170322522 0.164972041 #> 37 0.659825968 0.512125056 0.244236256 #> 38 0.316252812 0.243424104 0.121948520 #> 39 0.767161782 0.068322860 0.151467313 #> 40 0.160772582 0.166894471 0.137544868 #> 41 0.188735112 0.459799106 0.196474588 #> 42 0.583070912 0.386470347 0.062689557 #> 43 0.783560444 0.149149666 0.447776817 #> 44 0.267300835 0.150947637 0.458493755 #> 45 0.346929320 0.218012817 0.374821535 #> 46 0.238868726 0.213759981 0.098459503 #> 47 0.108704721 0.251100490 0.157438372 #> 48 0.271017234 0.265649875 0.028816112 #> 49 0.642955178 0.264533290 0.324776027 #> 50 0.014944438 0.058644003 0.164366976 #> 51 0.242317570 0.254305845 0.132662283 #> 52 0.872308848 0.735394607 0.210601353 #> 53 0.197396280 0.125416787 0.123836431 #> 54 0.463792939 0.636167444 0.719611121 #> 55 0.812413894 0.331767332 0.334587422 #> 56 0.603640851 0.113747550 0.020959544 #> 57 0.921265139 0.136453747 0.205670098 #> 58 0.148107737 0.431742615 0.319193582 #> 59 0.504541304 0.546537125 0.195445431 #> 60 0.390624639 0.236609665 0.139075380 #> 61 0.485899569 0.482753769 0.230418632 #> 62 0.360069878 0.022392161 0.283565883 #> 63 1.429045683 0.127620914 0.210564572 #> 64 0.334410275 0.710344397 0.055940397 #> 65 0.727814593 0.169040274 0.060761679 #> 66 0.006360511 0.239832412 0.161713209 #> 67 0.694355224 0.304868413 0.408470218 #> 68 0.039711039 0.185638475 0.399907082 #> 69 0.516133942 0.408771584 0.102536236 #> 70 0.270987552 0.107841396 0.223768853 #> 71 0.104533002 0.247681051 0.477045290 #> 72 0.439510393 0.618201782 0.555625254 #> 73 0.521413913 0.131314244 0.125807726 #> 74 0.392703120 0.055325380 0.030048250 #> 75 0.621183266 0.203023778 0.154136545 #> 76 0.666064314 0.207262980 0.032114604 #> 77 0.273934382 0.041482173 0.140717964 #> 78 0.174092248 0.023496185 0.243634236 #> 79 0.297974828 0.283801184 0.280012992 #> 80 0.857868045 0.409861702 0.100536495 #>
predict(modpls,type="scores",se.fit=TRUE)
#> Comp_1 Comp_2 Comp_3 #> 1 -1.51471639 -1.61173906 -0.44174968 #> 2 -0.56522390 -0.16546763 1.79666550 #> 3 1.37287931 0.23618504 0.17705140 #> 4 -0.02606437 0.77179724 0.18489957 #> 5 1.91441216 -1.07210272 0.08265437 #> 6 -0.46571063 -1.19453407 -0.55764274 #> 7 -1.79028814 0.82091488 -0.92020050 #> 8 3.07782184 0.26648299 -1.16036032 #> 9 -0.15611733 -0.62813331 -1.88561876 #> 10 -0.58333215 0.30338850 3.54063538 #> 11 1.41914382 0.85751237 -0.67156485 #> 12 -1.86673554 0.92912097 -0.14496294 #> 13 1.33236164 -1.44761991 0.20315139 #> 14 -1.29027208 0.66092409 -0.28088782 #> 15 0.44968325 -0.72430875 -1.29358311 #> 16 1.25790515 -2.24336748 0.01751807 #> 17 -1.71792143 -0.92400487 -0.64636970 #> 18 -1.81591875 -1.13822249 -0.92463946 #> 19 0.76125243 2.11676931 -2.31982404 #> 20 -1.88923160 0.76205763 -1.88198233 #> 21 -1.60663454 -1.25836174 2.52221645 #> 22 -0.23346928 -1.91878178 0.34001967 #> 23 0.57947215 -0.53958566 1.21467455 #> 24 0.64207759 0.40774841 -0.28892651 #> 25 -2.89381652 -1.08725524 -1.33802415 #> 26 -2.24287780 0.89841177 0.10996738 #> 27 2.61039185 0.11773530 1.02333549 #> 28 1.92254691 -1.13546701 -1.73760984 #> 29 -1.12260905 1.23886512 -0.07023379 #> 30 -2.44318998 0.02742624 0.78389936 #> 31 1.42245880 0.26943594 2.36644962 #> 32 -2.85910880 -0.71749249 2.32546733 #> 33 -1.91850027 -0.50288750 0.92871087 #> 34 0.22520089 1.39126815 -2.59049250 #> 35 2.42576411 -1.02255543 0.14180476 #> 36 -0.05537459 0.71091002 -0.92781969 #> 37 2.10439206 2.13756133 1.37360977 #> 38 -1.00862945 1.01602908 0.68585099 #> 39 -2.44671964 -0.28517313 0.85186771 #> 40 0.51275421 -0.69660167 0.77356645 #> 41 -0.60193549 1.91915778 1.10499325 #> 42 1.85959610 -1.61309051 -0.35257250 #> 43 -2.49902013 0.62253653 2.51834278 #> 44 -0.85250624 -0.63004109 2.57861593 #> 45 -1.10646646 0.90996478 -2.10803478 #> 46 0.76182732 -0.89221385 0.55374635 #> 47 -0.34669346 -1.04806959 -0.88544956 #> 48 0.86435900 -1.10879734 -0.16206477 #> 49 2.05058581 -1.10413682 1.82657371 #> 50 0.04766250 0.24477450 -0.92441674 #> 51 -0.77282677 1.06144844 -0.74610629 #> 52 2.78206663 -3.06946722 1.18444362 #> 53 0.62955868 -0.52347775 -0.69646880 #> 54 1.47918121 2.65530247 4.04716679 #> 55 2.59104283 1.38476532 1.88175400 #> 56 -1.92520009 -0.47477147 0.11787862 #> 57 -2.93820361 -0.56954497 -1.15670974 #> 58 -0.47236205 1.80205266 1.79517746 #> 59 -1.60914054 2.28119403 1.09920516 #> 60 -1.24582455 0.98758626 -0.78217421 #> 61 1.54968619 2.01496837 -1.29589803 #> 62 1.14837582 -0.09346275 1.59480363 #> 63 4.55767509 0.53267757 -1.18423676 #> 64 1.06653930 -2.96491002 -0.31461453 #> 65 2.32122911 0.70555804 -0.34172992 #> 66 -0.02028567 1.00103770 -0.90949169 #> 67 -2.21451669 -1.27249179 -2.29727842 #> 68 0.12665096 -0.77483736 -2.24911847 #> 69 1.64611310 1.70617376 -0.57667431 #> 70 -0.86426433 -0.45011974 -1.25849899 #> 71 -0.33338854 1.03379717 2.68295167 #> 72 1.40173655 2.58031550 -3.12489345 #> 73 1.66295258 -0.54809318 0.70755556 #> 74 -1.25245347 0.23092288 0.16899444 #> 75 1.98114835 -0.84740196 -0.86687975 #> 76 2.12428810 -0.86509599 0.18061583 #> 77 -0.87366270 0.17314265 -0.79141227 #> 78 -0.55523481 -0.09807084 -1.37022395 #> 79 -0.95033523 1.18455918 -1.57482181 #> 80 -2.73601038 -1.71072380 0.56542757
#Identical to predict(modpls,type="lp") predict(modpls,newdata=X_train_micro[1:5,],type="risk")
#> 1 2 3 4 5 #> 0.02001048 0.98902638 9.92327996 2.16822181 6.15907848
#predict(modpls,newdata=X_train_micro[1:5,],type="expected") predict(modpls,newdata=X_train_micro[1:5,],type="terms")
#> tt.1 tt.2 tt.3 #> 1 -2.18710337 -1.4888633 -0.23553228 #> 2 -0.81612842 -0.1528527 0.95794687 #> 3 1.98230440 0.2181788 0.09440034 #> 4 -0.03763442 0.7129570 0.09858483 #> 5 2.76422524 -0.9903678 0.04406969 #> attr(,"constant") #> [1] -3.218013e-16
predict(modpls,newdata=X_train_micro[1:5,],type="scores")
#> Comp_1 Comp_2 Comp_3 #> [1,] -1.51471639 -1.6117391 -0.44174968 #> [2,] -0.56522390 -0.1654676 1.79666550 #> [3,] 1.37287931 0.2361850 0.17705140 #> [4,] -0.02606437 0.7717972 0.18489957 #> [5,] 1.91441216 -1.0721027 0.08265437
#Identical to predict(modpls,type="lp") predict(modpls,newdata=X_train_micro[1:5,],type="risk",se.fit=TRUE)
#> $fit #> 1 2 3 4 5 #> 0.02001048 0.98902638 9.92327996 2.16822181 6.15907848 #> #> $se.fit #> 1 2 3 4 5 #> 0.1154615 0.2748572 1.5252479 0.2864182 1.2258577 #>
#predict(modpls,newdata=X_train_micro[1:5,],type="expected",se.fit=TRUE) predict(modpls,newdata=X_train_micro[1:5,],type="terms",se.fit=TRUE)
#> $fit #> tt.1 tt.2 tt.3 #> 1 -2.18710337 -1.4888633 -0.23553228 #> 2 -0.81612842 -0.1528527 0.95794687 #> 3 1.98230440 0.2181788 0.09440034 #> 4 -0.03763442 0.7129570 0.09858483 #> 5 2.76422524 -0.9903678 0.04406969 #> attr(,"constant") #> [1] -3.218013e-16 #> #> $se.fit #> tt.1 tt.2 tt.3 #> 1 0.474934889 0.38614656 0.07854581 #> 2 0.177224300 0.03964336 0.31945816 #> 3 0.430462288 0.05658611 0.03148083 #> 4 0.008172408 0.18491011 0.03287628 #> 5 0.600258328 0.25685844 0.01469646 #>
predict(modpls,newdata=X_train_micro[1:5,],type="scores")
#> Comp_1 Comp_2 Comp_3 #> [1,] -1.51471639 -1.6117391 -0.44174968 #> [2,] -0.56522390 -0.1654676 1.79666550 #> [3,] 1.37287931 0.2361850 0.17705140 #> [4,] -0.02606437 0.7717972 0.18489957 #> [5,] 1.91441216 -1.0721027 0.08265437
predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=1)
#> 1 2 3 4 5 #> 0.1122414 0.4421401 7.2594524 0.9630650 15.8667424
predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=2)
#> 1 2 3 4 5 #> 0.02532491 0.37946947 9.02937514 1.96466659 5.89354378
predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=3)
#> 1 2 3 4 5 #> 0.02001048 0.98902638 9.92327996 2.16822181 6.15907848
try(predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=4))
#> Error in predict.plsRcoxmodel(modpls, newdata = X_train_micro[1:5, ], : #> Cannot predict using more components than extracted.
predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=1)
#> tt.1 tt.2 tt.3 #> 1 -2.18710337 1.461454e-16 -2.071822e-17 #> 2 -0.81612842 1.461454e-16 -2.071822e-17 #> 3 1.98230440 1.461454e-16 -2.071822e-17 #> 4 -0.03763442 1.461454e-16 -2.071822e-17 #> 5 2.76422524 1.461454e-16 -2.071822e-17 #> attr(,"constant") #> [1] -3.218013e-16
predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=2)
#> tt.1 tt.2 tt.3 #> 1 -2.18710337 -1.4888633 -2.071822e-17 #> 2 -0.81612842 -0.1528527 -2.071822e-17 #> 3 1.98230440 0.2181788 -2.071822e-17 #> 4 -0.03763442 0.7129570 -2.071822e-17 #> 5 2.76422524 -0.9903678 -2.071822e-17 #> attr(,"constant") #> [1] -3.218013e-16
predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=3)
#> tt.1 tt.2 tt.3 #> 1 -2.18710337 -1.4888633 -0.23553228 #> 2 -0.81612842 -0.1528527 0.95794687 #> 3 1.98230440 0.2181788 0.09440034 #> 4 -0.03763442 0.7129570 0.09858483 #> 5 2.76422524 -0.9903678 0.04406969 #> attr(,"constant") #> [1] -3.218013e-16
try(predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=4))
#> Error in predict.plsRcoxmodel(modpls, newdata = X_train_micro[1:5, ], : #> Cannot predict using more components than extracted.
predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=1)
#> Comp_1 #> [1,] -1.51471639 #> [2,] -0.56522390 #> [3,] 1.37287931 #> [4,] -0.02606437 #> [5,] 1.91441216
predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=2)
#> Comp_1 Comp_2 #> [1,] -1.51471639 -1.6117391 #> [2,] -0.56522390 -0.1654676 #> [3,] 1.37287931 0.2361850 #> [4,] -0.02606437 0.7717972 #> [5,] 1.91441216 -1.0721027
predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=3)
#> Comp_1 Comp_2 Comp_3 #> [1,] -1.51471639 -1.6117391 -0.44174968 #> [2,] -0.56522390 -0.1654676 1.79666550 #> [3,] 1.37287931 0.2361850 0.17705140 #> [4,] -0.02606437 0.7717972 0.18489957 #> [5,] 1.91441216 -1.0721027 0.08265437
try(predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=4))
#> Error in predict.plsRcoxmodel(modpls, newdata = X_train_micro[1:5, ], : #> Cannot predict using more components than extracted.