R/plsRcox.R, R/plsRcoxmodel.default.R, R/plsRcoxmodel.formula.R
plsRcox.RdThis function implements an extension of Partial least squares Regression to Cox Models.
plsRcox(Xplan, ...) plsRcoxmodel(Xplan, ...) # S3 method for default plsRcoxmodel( Xplan, time, time2, event, type, origin, typeres = "deviance", collapse, weighted, scaleX = TRUE, scaleY = TRUE, nt = min(2, ncol(Xplan)), limQ2set = 0.0975, dataPredictY = Xplan, pvals.expli = FALSE, alpha.pvals.expli = 0.05, tol_Xi = 10^(-12), weights, control, sparse = FALSE, sparseStop = TRUE, allres = TRUE, verbose = TRUE, ... ) # S3 method for formula plsRcoxmodel( Xplan, time, time2, event, type, origin, typeres = "deviance", collapse, weighted, scaleX = TRUE, scaleY = NULL, dataXplan = NULL, nt = min(2, ncol(Xplan)), limQ2set = 0.0975, dataPredictY = Xplan, pvals.expli = FALSE, model_frame = FALSE, alpha.pvals.expli = 0.05, tol_Xi = 10^(-12), weights, subset, control, sparse = FALSE, sparseStop = TRUE, allres = TRUE, verbose = TRUE, ... )
| Xplan | a formula or a matrix with the eXplanatory variables (training) dataset |
|---|---|
| ... | arguments to pass to |
| time | for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval. |
| time2 | The status indicator, normally 0=alive, 1=dead. Other choices
are |
| event | ending time of the interval for interval censored or counting
process data only. Intervals are assumed to be open on the left and closed
on the right, |
| type | character string specifying the type of censoring. Possible
values are |
| origin | for counting process data, the hazard function origin. This option was intended to be used in conjunction with a model containing time dependent strata in order to align the subjects properly when they cross over from one strata to another, but it has rarely proven useful. |
| typeres | character string indicating the type of residual desired.
Possible values are |
| collapse | vector indicating which rows to collapse (sum) over. In
time-dependent models more than one row data can pertain to a single
individual. If there were 4 individuals represented by 3, 1, 2 and 4 rows of
data respectively, then |
| weighted | if |
| scaleX | Should the |
| scaleY | Should the |
| nt | number of components to be extracted |
| limQ2set | limit value for the Q2 |
| dataPredictY | predictor(s) (testing) dataset |
| pvals.expli | should individual p-values be reported to tune model selection ? |
| alpha.pvals.expli | level of significance for predictors when pvals.expli=TRUE |
| tol_Xi | minimal value for Norm2(Xi) and \(\mathrm{det}(pp' \times
pp)\) if there is any missing value in the |
| weights | an optional vector of 'prior weights' to be used in the
fitting process. Should be |
| control | a list of parameters for controlling the fitting process. For
|
| sparse | should the coefficients of non-significant predictors
(< |
| sparseStop | should component extraction stop when no significant
predictors (< |
| allres | FALSE to return only the Cox model and TRUE for additionnal results. See details. Defaults to FALSE. |
| verbose | Should some details be displayed ? |
| dataXplan | an optional data frame, list or environment (or object
coercible by |
| model_frame | If |
| subset | an optional vector specifying a subset of observations to be used in the fitting process. |
| method | the method to be used in fitting the model. The default method
|
Depends on the model that was used to fit the model.
A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.
A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.
The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula.
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.
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.
Frédéric Bertrand
frederic.bertrand@math.unistra.fr
http://www-irma.u-strasbg.fr/~fbertran/
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,] X_train_micro_df <- data.frame(X_train_micro) Y_train_micro <- micro.censure$survyear[1:80] C_train_micro <- micro.censure$DC[1:80] plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)#> ____************************************************____ #> ____Component____ 1 ____ #> ____Component____ 2 ____ #> ____Component____ 3 ____ #> ____Component____ 4 ____ #> ____Component____ 5 ____ #> ____Predicting X without NA neither in X nor in Y____ #> ****________________________________________________**** #>#> Number of required components: #> [1] 5 #> Number of successfully computed components: #> [1] 5 #> Coefficients: #> [,1] #> D18S61 0.68964919 #> D17S794 -1.14362392 #> D13S173 1.37632457 #> D20S107 4.96128745 #> TP53 1.68453950 #> D9S171 -1.46691252 #> D8S264 0.66710776 #> D5S346 -4.61338196 #> D22S928 -1.82005524 #> D18S53 0.79853646 #> D1S225 -1.46234986 #> D3S1282 -1.67925042 #> D15S127 3.92225537 #> D1S305 -2.29680161 #> D1S207 2.02539691 #> D2S138 -3.48975878 #> D16S422 -2.92189625 #> D9S179 -0.59484679 #> D10S191 -1.30136747 #> D4S394 1.34265359 #> D1S197 -0.75014044 #> D6S264 1.32746604 #> D14S65 -3.20882866 #> D17S790 0.55427680 #> D5S430 3.40654627 #> D3S1283 2.12510239 #> D4S414 2.73619967 #> D8S283 0.71955323 #> D11S916 1.45026508 #> D2S159 0.90293134 #> D16S408 -0.59719901 #> D6S275 -1.02204186 #> D10S192 1.14220367 #> sexe 0.67314561 #> Agediag 0.04908478 #> Siege -0.41985924 #> T 2.70581463 #> N 2.47039973 #> M -4.53213922 #> STADE 0.48221697 #> Information criteria and Fit statistics: #> AIC BIC #> Nb_Comp_0 112.87990 112.87990 #> Nb_Comp_1 85.11075 87.49278 #> Nb_Comp_2 75.49537 80.25942 #> Nb_Comp_3 68.45852 75.60460 #> Nb_Comp_4 63.09284 72.62094 #> Nb_Comp_5 55.30567 67.21581plsRcox(~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)#> Warning: non-list contrasts argument ignored#> ____************************************************____ #> ____Component____ 1 ____ #> ____Component____ 2 ____ #> ____Component____ 3 ____ #> ____Component____ 4 ____ #> ____Component____ 5 ____ #> ____Predicting X without NA neither in X nor in Y____ #> ****________________________________________________**** #>#> Number of required components: #> [1] 5 #> Number of successfully computed components: #> [1] 5 #> Coefficients: #> [,1] #> X_train_microD18S61 0.68964919 #> X_train_microD17S794 -1.14362392 #> X_train_microD13S173 1.37632457 #> X_train_microD20S107 4.96128745 #> X_train_microTP53 1.68453950 #> X_train_microD9S171 -1.46691252 #> X_train_microD8S264 0.66710776 #> X_train_microD5S346 -4.61338196 #> X_train_microD22S928 -1.82005524 #> X_train_microD18S53 0.79853646 #> X_train_microD1S225 -1.46234986 #> X_train_microD3S1282 -1.67925042 #> X_train_microD15S127 3.92225537 #> X_train_microD1S305 -2.29680161 #> X_train_microD1S207 2.02539691 #> X_train_microD2S138 -3.48975878 #> X_train_microD16S422 -2.92189625 #> X_train_microD9S179 -0.59484679 #> X_train_microD10S191 -1.30136747 #> X_train_microD4S394 1.34265359 #> X_train_microD1S197 -0.75014044 #> X_train_microD6S264 1.32746604 #> X_train_microD14S65 -3.20882866 #> X_train_microD17S790 0.55427680 #> X_train_microD5S430 3.40654627 #> X_train_microD3S1283 2.12510239 #> X_train_microD4S414 2.73619967 #> X_train_microD8S283 0.71955323 #> X_train_microD11S916 1.45026508 #> X_train_microD2S159 0.90293134 #> X_train_microD16S408 -0.59719901 #> X_train_microD6S275 -1.02204186 #> X_train_microD10S192 1.14220367 #> X_train_microsexe 0.67314561 #> X_train_microAgediag 0.04908478 #> X_train_microSiege -0.41985924 #> X_train_microT 2.70581463 #> X_train_microN 2.47039973 #> X_train_microM -4.53213922 #> X_train_microSTADE 0.48221697 #> Information criteria and Fit statistics: #> AIC BIC #> Nb_Comp_0 112.87990 112.87990 #> Nb_Comp_1 85.11075 87.49278 #> Nb_Comp_2 75.49537 80.25942 #> Nb_Comp_3 68.45852 75.60460 #> Nb_Comp_4 63.09284 72.62094 #> Nb_Comp_5 55.30567 67.21581plsRcox(Xplan=X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)#> ____************************************************____ #> ____Component____ 1 ____ #> ____Component____ 2 ____ #> ____Component____ 3 ____ #> Warning : 25 < 10^{-12} #> Warning only 3 components could thus be extracted #> ____Predicting X without NA neither in X nor in Y____ #> ****________________________________________________**** #>#> Number of required components: #> [1] 5 #> Number of successfully computed components: #> [1] 3 #> Coefficients: #> [,1] #> D18S61 0.00000000 #> D17S794 0.00000000 #> D13S173 0.00000000 #> D20S107 2.22871454 #> TP53 0.00000000 #> D9S171 0.00000000 #> D8S264 0.00000000 #> D5S346 -1.20298526 #> D22S928 0.00000000 #> D18S53 0.00000000 #> D1S225 -1.29459798 #> D3S1282 -1.99426291 #> D15S127 1.39645601 #> D1S305 0.00000000 #> D1S207 1.25164327 #> D2S138 -1.65740160 #> D16S422 0.00000000 #> D9S179 0.00000000 #> D10S191 -1.25360805 #> D4S394 0.00000000 #> D1S197 0.00000000 #> D6S264 0.00000000 #> D14S65 -1.33587373 #> D17S790 0.00000000 #> D5S430 1.72799213 #> D3S1283 0.00000000 #> D4S414 1.03558702 #> D8S283 0.00000000 #> D11S916 0.00000000 #> D2S159 0.00000000 #> D16S408 -1.75748257 #> D6S275 0.00000000 #> D10S192 0.00000000 #> sexe 0.00000000 #> Agediag 0.05075304 #> Siege 0.00000000 #> T 1.36569407 #> N 1.27485618 #> M -1.17682617 #> STADE -0.65106093 #> Information criteria and Fit statistics: #> AIC BIC #> Nb_Comp_0 112.87990 112.87990 #> Nb_Comp_1 85.54313 87.92516 #> Nb_Comp_2 75.16125 79.92530 #> Nb_Comp_3 73.63097 80.77705plsRcox(Xplan=~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)#> Warning: non-list contrasts argument ignored#> ____************************************************____ #> ____Component____ 1 ____ #> ____Component____ 2 ____ #> ____Component____ 3 ____ #> Warning : 25 < 10^{-12} #> Warning only 3 components could thus be extracted #> ____Predicting X without NA neither in X nor in Y____ #> ****________________________________________________**** #>#> Number of required components: #> [1] 5 #> Number of successfully computed components: #> [1] 3 #> Coefficients: #> [,1] #> X_train_microD18S61 0.00000000 #> X_train_microD17S794 0.00000000 #> X_train_microD13S173 0.00000000 #> X_train_microD20S107 2.22871454 #> X_train_microTP53 0.00000000 #> X_train_microD9S171 0.00000000 #> X_train_microD8S264 0.00000000 #> X_train_microD5S346 -1.20298526 #> X_train_microD22S928 0.00000000 #> X_train_microD18S53 0.00000000 #> X_train_microD1S225 -1.29459798 #> X_train_microD3S1282 -1.99426291 #> X_train_microD15S127 1.39645601 #> X_train_microD1S305 0.00000000 #> X_train_microD1S207 1.25164327 #> X_train_microD2S138 -1.65740160 #> X_train_microD16S422 0.00000000 #> X_train_microD9S179 0.00000000 #> X_train_microD10S191 -1.25360805 #> X_train_microD4S394 0.00000000 #> X_train_microD1S197 0.00000000 #> X_train_microD6S264 0.00000000 #> X_train_microD14S65 -1.33587373 #> X_train_microD17S790 0.00000000 #> X_train_microD5S430 1.72799213 #> X_train_microD3S1283 0.00000000 #> X_train_microD4S414 1.03558702 #> X_train_microD8S283 0.00000000 #> X_train_microD11S916 0.00000000 #> X_train_microD2S159 0.00000000 #> X_train_microD16S408 -1.75748257 #> X_train_microD6S275 0.00000000 #> X_train_microD10S192 0.00000000 #> X_train_microsexe 0.00000000 #> X_train_microAgediag 0.05075304 #> X_train_microSiege 0.00000000 #> X_train_microT 1.36569407 #> X_train_microN 1.27485618 #> X_train_microM -1.17682617 #> X_train_microSTADE -0.65106093 #> Information criteria and Fit statistics: #> AIC BIC #> Nb_Comp_0 112.87990 112.87990 #> Nb_Comp_1 85.54313 87.92516 #> Nb_Comp_2 75.16125 79.92530 #> Nb_Comp_3 73.63097 80.77705