R/coxpls.R
, R/coxpls.default.R
, R/coxpls.formula.R
coxpls.Rd
This function computes the Cox Model based on PLSR components computed model with
as explanatory variables: Xplan.
It uses the package mixOmics
to perform PLSR
fit.
coxpls(Xplan, ...) # S3 method for default coxpls( Xplan, time, time2, event, type, origin, typeres = "deviance", collapse, weighted, scaleX = TRUE, scaleY = TRUE, ncomp = min(7, ncol(Xplan)), modepls = "regression", plot = FALSE, allres = FALSE, ... ) # S3 method for formula coxpls( Xplan, time, time2, event, type, origin, typeres = "deviance", collapse, weighted, scaleX = TRUE, scaleY = TRUE, ncomp = min(7, ncol(Xplan)), modepls = "regression", plot = FALSE, allres = FALSE, dataXplan = NULL, subset, weights, model_frame = FALSE, ... )
Xplan | a formula or a matrix with the eXplanatory variables (training) dataset |
---|---|
... | Arguments to be passed on 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 |
ncomp | The number of components to include in the model. It this is not supplied, min(7,maximal number) components is used. |
modepls | character string. What type of algorithm to use, (partially)
matching one of "regression", "canonical", "invariant" or "classic". See
|
plot | Should the survival function be plotted ?) |
allres | FALSE to return only the Cox model and TRUE for additionnal results. See details. Defaults to FALSE. |
dataXplan | an optional data frame, list or environment (or object
coercible by |
subset | an optional vector specifying a subset of observations to be used in the fitting process. |
weights | an optional vector of 'prior weights' to be used in the
fitting process. Should be |
model_frame | If |
If allres=FALSE
:
Final Cox-model.
PLSR components.
Final Cox-model.
The PLSR model.
If allres=FALSE
returns only the final Cox-model. If
allres=TRUE
returns a list with the PLS components, the final
Cox-model and the PLSR model. allres=TRUE
is useful for evluating
model prediction accuracy on a test sample.
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] (cox_pls_fit=coxpls(X_train_micro,Y_train_micro,C_train_micro,ncomp=6))#> Call: #> coxph(formula = YCsurv ~ ., data = tt_pls) #> #> coef exp(coef) se(coef) z p #> dim.1 -0.5771 0.5615 0.2266 -2.546 0.0109 #> dim.2 -0.6350 0.5299 0.2798 -2.269 0.0233 #> dim.3 -0.5675 0.5670 0.2358 -2.407 0.0161 #> dim.4 0.2900 1.3365 0.2897 1.001 0.3167 #> dim.5 -0.3797 0.6841 0.2272 -1.671 0.0947 #> dim.6 0.2398 1.2710 0.2790 0.860 0.3899 #> #> Likelihood ratio test=26.12 on 6 df, p=0.0002119 #> n= 80, number of events= 17(cox_pls_fit=coxpls(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6))#> Warning: non-list contrasts argument ignored#> Call: #> coxph(formula = YCsurv ~ ., data = tt_pls) #> #> coef exp(coef) se(coef) z p #> dim.1 -0.5771 0.5615 0.2266 -2.546 0.0109 #> dim.2 -0.6350 0.5299 0.2798 -2.269 0.0233 #> dim.3 -0.5675 0.5670 0.2358 -2.407 0.0161 #> dim.4 0.2900 1.3365 0.2897 1.001 0.3167 #> dim.5 -0.3797 0.6841 0.2272 -1.671 0.0947 #> dim.6 0.2398 1.2710 0.2790 0.860 0.3899 #> #> Likelihood ratio test=26.12 on 6 df, p=0.0002119 #> n= 80, number of events= 17(cox_pls_fit=coxpls(~.,Y_train_micro,C_train_micro,ncomp=6,dataXplan=X_train_micro_df))#> Warning: non-list contrasts argument ignored#> Call: #> coxph(formula = YCsurv ~ ., data = tt_pls) #> #> coef exp(coef) se(coef) z p #> dim.1 -0.5771 0.5615 0.2266 -2.546 0.0109 #> dim.2 -0.6350 0.5299 0.2798 -2.269 0.0233 #> dim.3 -0.5675 0.5670 0.2358 -2.407 0.0161 #> dim.4 0.2900 1.3365 0.2897 1.001 0.3167 #> dim.5 -0.3797 0.6841 0.2272 -1.671 0.0947 #> dim.6 0.2398 1.2710 0.2790 0.860 0.3899 #> #> Likelihood ratio test=26.12 on 6 df, p=0.0002119 #> n= 80, number of events= 17