R/coxsplsDR.R
, R/coxsplsDR.default.R
, R/coxsplsDR.formula.R
coxsplsDR.Rd
This function computes the Cox Model based on sPLSR components computed model with
as the response: the Residuals of a CoxModel fitted with no covariate
as explanatory variables: Xplan.
It uses
the package spls
to perform the first step in SPLSR then
mixOmics
to perform PLSR step fit.
coxsplsDR(Xplan, ...) # S3 method for default coxsplsDR( 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, eta = 0.5, trace = FALSE, ... ) # S3 method for formula coxsplsDR( 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, eta = 0.5, trace = 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. The number of components to fit is specified with the argument ncomp. It this is not supplied, the maximal number of 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. 
eta  Thresholding parameter. 
trace  Print out the progress of variable selection? 
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.
sPLSR components.
Final Coxmodel.
The sPLSR model.
If allres=FALSE
returns only the final Coxmodel. If
allres=TRUE
returns a list with the sPLS components, the final
Coxmodel and the sPLSR 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_splsDR_fit=coxsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,eta=.5))#> Call: #> coxph(formula = YCsurv ~ ., data = tt_splsDR) #> #> coef exp(coef) se(coef) z p #> dim.1 0.8093 2.2462 0.2029 3.989 6.63e05 #> dim.2 0.9295 2.5333 0.2939 3.163 0.00156 #> dim.3 0.9968 2.7096 0.4190 2.379 0.01736 #> dim.4 0.9705 2.6391 0.3793 2.558 0.01052 #> dim.5 0.2162 1.2413 0.2811 0.769 0.44192 #> dim.6 0.4380 1.5496 0.3608 1.214 0.22473 #> #> Likelihood ratio test=55.06 on 6 df, p=4.51e10 #> n= 80, number of events= 17(cox_splsDR_fit2=coxsplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,eta=.5,trace=TRUE))#> Warning: nonlist contrasts argument ignored#> The variables that join the set of selected variables at each step: #>  1th step (K=1): #> X_train_microD20S107 X_train_microD5S346 X_train_microD1S225 X_train_microD3S1282 X_train_microD15S127 X_train_microD1S207 X_train_microD2S138 X_train_microD10S191 X_train_microD14S65 X_train_microD4S414 #> X_train_microD16S408 X_train_microT X_train_microN X_train_microSTADE #>  2th step (K=2): #> X_train_microD22S928 X_train_microD16S422 X_train_microD3S1283 X_train_microAgediag X_train_microM #>  3th step (K=3): #> X_train_microD1S305 X_train_microD8S283 X_train_microD10S192 X_train_microsexe X_train_microSiege #>  4th step (K=4): #> X_train_microD17S794 X_train_microD13S173 X_train_microTP53 X_train_microD6S264 X_train_microD2S159 X_train_microD6S275 #>  5th step (K=5): #> X_train_microD18S61 X_train_microD9S171 X_train_microD8S264 X_train_microD18S53 X_train_microD4S394 X_train_microD11S916 #>  6th step (K=6): #> X_train_microD17S790#> Call: #> coxph(formula = YCsurv ~ ., data = tt_splsDR) #> #> coef exp(coef) se(coef) z p #> dim.1 0.8093 2.2462 0.2029 3.989 6.63e05 #> dim.2 0.9295 2.5333 0.2939 3.163 0.00156 #> dim.3 0.9968 2.7096 0.4190 2.379 0.01736 #> dim.4 0.9705 2.6391 0.3793 2.558 0.01052 #> dim.5 0.2162 1.2413 0.2811 0.769 0.44192 #> dim.6 0.4380 1.5496 0.3608 1.214 0.22473 #> #> Likelihood ratio test=55.06 on 6 df, p=4.51e10 #> n= 80, number of events= 17(cox_splsDR_fit3=coxsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6, dataXplan=X_train_micro_df,eta=.5))#> Warning: nonlist contrasts argument ignored#> Call: #> coxph(formula = YCsurv ~ ., data = tt_splsDR) #> #> coef exp(coef) se(coef) z p #> dim.1 0.8093 2.2462 0.2029 3.989 6.63e05 #> dim.2 0.9295 2.5333 0.2939 3.163 0.00156 #> dim.3 0.9968 2.7096 0.4190 2.379 0.01736 #> dim.4 0.9705 2.6391 0.3793 2.558 0.01052 #> dim.5 0.2162 1.2413 0.2811 0.769 0.44192 #> dim.6 0.4380 1.5496 0.3608 1.214 0.22473 #> #> Likelihood ratio test=55.06 on 6 df, p=4.51e10 #> n= 80, number of events= 17