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
timedependent 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 Coxmodel.
PLSR components.
Final Coxmodel.
The PLSR model.
If allres=FALSE
returns only the final Coxmodel. If
allres=TRUE
returns a list with the PLS components, the final
Coxmodel and the PLSR model. allres=TRUE
is useful for evluating
model prediction accuracy on a test sample.
plsRcox, CoxModels in a high dimensional setting in R, Frederic
Bertrand, Philippe Bastien, Nicolas Meyer and Myriam MaumyBertrand (2014).
Proceedings of User2014!, Los Angeles, page 152.
Deviance residualsbased sparse PLS and sparse kernel PLS regression for censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam MaumyBertrand (2015), Bioinformatics, 31(3):397404, doi:10.1093/bioinformatics/btu660.
Frédéric Bertrand
frederic.bertrand@math.unistra.fr
http://wwwirma.ustrasbg.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: nonlist 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: nonlist 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