plsRcox (version 0.7.6)

coxDKplsDR: Fitting a Direct Kernel PLS model on the (Deviance) Residuals

Description

This function computes the Direct Kernel PLSR model with the Residuals of a Cox-Model fitted with an intercept as the only explanatory variable as the response and Xplan as explanatory variables. Default behaviour uses the Deviance residuals.

Usage

coxDKplsDR(Xplan, ...)
## S3 method for class 'default':
coxDKplsDR(Xplan,time,time2,event,type,origin,typeres="deviance", collapse, weighted, scaleX=TRUE, scaleY=TRUE, ncomp=min(7,ncol(Xplan)), validation = "CV", plot=FALSE, allres=FALSE, kernel="rbfdot",hyperkernel,...)
## S3 method for class 'formula':
coxDKplsDR(Xplan,time,time2,event,type,origin,typeres="deviance", collapse, weighted, scaleX=TRUE, scaleY=TRUE, ncomp=min(7,ncol(Xplan)), validation = "CV", plot=FALSE, allres=FALSE,dataXplan=NULL,subset,weights,model_frame=FALSE, kernel="rbfdot",hyperkernel,...)

Arguments

Xplan

a formula or a matrix with the eXplanatory variables (training) dataset

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 TRUE/FALSE (TRUE = death) or 1/2 (2=death). For interval censored data, the status indicator is 0=right censored, 1=event at time, 2=left censored, 3=

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, (start, end]. For counting process data, event indicates whether an event occurred at the e

type

character string specifying the type of censoring. Possible values are "right", "left", "counting", "interval", or "interval2". The default is "right" or "counting"

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 rar

typeres

character string indicating the type of residual desired. Possible values are "martingale", "deviance", "score", "schoenfeld", "dfbeta", "dfbetas", and "scaledsch".

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 collapse=c(1,1,1,2,3,3,

weighted

if TRUE and the model was fit with case weights, then the weighted residuals are returned.

scaleX

Should the Xplan columns be standardized ?

scaleY

Should the time values be standardized ?

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 (taking account of any cross-validation).

validation

character. What kind of (internal) validation to use. If validation = "CV", cross-validation is performed. The number and type of cross-validation segments are specified with the arguments segments and segment.type.

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 as.data.frame to a data frame) containing the variables in the model. If not found in dataXplan, the variables are take

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 NULL or a numeric vector.

model_frame

If TRUE, the model frame is returned.

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes the inner product in feature space between two vector arguments (see kernels). The <

hyperkernel

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. For valid parameters for existing kernels are :

• sigma, inverse kernel width for the Radial Basis k

...

Arguments to be passed on to survival::coxph.

Value

• If allres=FALSE :
• cox_DKplsDR
  Final Cox-model.
• If allres=TRUE :
• tt_DKplsDR
  PLSR components.
• cox_DKplsDR
  Final Cox-model.
• DKplsDR_mod
  The PLSR model.

Details

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.

References

plsRcox : mod�les{mod`eles} de Cox en pr�sence{pr'esence} d'un grand nombre de variables explicatives, Fr�d�ric{Fr'ed'eric} Bertrand, Myriam Maumy-Bertrand, Marie-Pierre Gaub, Nicolas Meyer, Chimiom�trie{Chimiom'etrie} 2010, Paris, 2010.

See Also

coxph, plsr

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,]
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_DKplsDR_fit=coxDKplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV"))

#Fixing sigma to compare with plsDR on Gram matrix; should be identical
(cox_DKplsDR_fit=coxDKplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV",hyperkernel=list(sigma=0.01292786)))

library(kernlab)
X_train_micro_kern <- kernelMatrix(rbfdot(sigma=0.01292786),scale(X_train_micro))
(cox_DKplsDR_fit2=coxplsDR(~X_train_micro_kern,Y_train_micro,C_train_micro,ncomp=6,validation="CV",scaleX=FALSE))
detach(package:kernlab)

(cox_DKplsDR_fit=coxDKplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV",kernel="laplacedot",hyperkernel=list(sigma=0.01292786)))

library(kernlab)
X_train_micro_kern <- kernelMatrix(laplacedot(sigma=0.01292786),scale(X_train_micro))
(cox_DKplsDR_fit2=coxplsDR(~X_train_micro_kern,Y_train_micro,C_train_micro,ncomp=6,validation="CV",scaleX=FALSE))
detach(package:kernlab)

(cox_DKplsDR_fit=coxDKplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV"))
(cox_DKplsDR_fit=coxDKplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,validation="CV",dataXplan=X_train_micro_df))

(cox_DKplsDR_fit=coxDKplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV",allres=TRUE))
(cox_DKplsDR_fit=coxDKplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV",allres=TRUE))
(cox_DKplsDR_fit=coxDKplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,validation="CV",allres=TRUE,dataXplan=X_train_micro_df))


rm(X_train_micro,Y_train_micro,C_train_micro,cox_DKplsDR_fit)