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

coxDKplsDR {plsRcox}

R Documentation

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

Description

This function computes the Cox Model based on PLSR components computed model with

as the response: the Residuals of a Cox-Model fitted with no covariate
as explanatory variables: a Kernel transform of Xplan.

It uses the package kernlab to compute the Kernel transforms of Xplan, then the package mixOmics to perform PLSR fit.

Usage

coxDKplsDR(Xplan, ...)

## Default S3 method:
coxDKplsDR(
  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,
  kernel = "rbfdot",
  hyperkernel,
  verbose = TRUE,
  ...
)

## 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)),
  modepls = "regression",
  plot = FALSE,
  allres = FALSE,
  dataXplan = NULL,
  subset,
  weights,
  model_frame = FALSE,
  kernel = "rbfdot",
  hyperkernel,
  verbose = TRUE,
  model_matrix = FALSE,
  contrasts.arg = NULL,
  ...
)

Arguments

`Xplan`	a formula or a matrix with the eXplanatory variables (training) dataset
`...`	Arguments to be passed on to `survival::coxph`.
`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=interval censored. Although unusual, the event indicator can be omitted, in which case all subjects are assumed to have an event.
`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 end of the interval.
`type`	character string specifying the type of censoring. Possible values are `"right"`, `"left"`, `"counting"`, `"interval"`, or `"interval2"`. The default is `"right"` or `"counting"` depending on whether the `time2` argument is absent or present, respectively.
`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 `"martingale"`, `"deviance"`, `"score"`, `"schoenfeld"`, `"dfbeta"`, `"dfbetas"`, and `"scaledsch"`. Only enough of the string to determine a unique match is required.
`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(1,1,1,2,3,3,4,4,4,4)` could be used to obtain per subject rather than per observation residuals.
`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.
`modepls`	character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See `pls` for details
`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.
`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 `kernlab` package provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: list("rbfdot") Radial Basis kernel "Gaussian" list("polydot") Polynomial kernel list("vanilladot") Linear kernel list("tanhdot") Hyperbolic tangent kernel list("laplacedot") Laplacian kernel list("besseldot") Bessel kernel list("anovadot") ANOVA RBF kernel list("splinedot") Spline kernel
`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 kernel function "rbfdot" and the Laplacian kernel "laplacedot". `degree`, `scale`, `offset` for the Polynomial kernel "polydot". `scale`, offset for the Hyperbolic tangent kernel function "tanhdot". `sigma`, `order`, `degree` for the Bessel kernel "besseldot". `sigma`, `degree` for the ANOVA kernel "anovadot". In the case of a Radial Basis kernel function (Gaussian) or Laplacian kernel, if `hyperkernel` is missing, the heuristics in sigest are used to calculate a good sigma value from the data.
`verbose`	Should some details be displayed ?
`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 taken from `environment(Xplan)`, typically the environment from which `coxDKplsDR` is called.
`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.
`model_matrix`	If `TRUE`, the model matrix is returned.
`contrasts.arg`	a list, whose entries are values (numeric matrices, functions or character strings naming functions) to be used as replacement values for the contrasts replacement function and whose names are the names of columns of data containing factors.

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.

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.

Author(s)

Frédéric Bertrand
frederic.bertrand@utt.fr
http://www-irma.u-strasbg.fr/~fbertran/

References

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.

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))

#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,
hyperkernel=list(sigma=0.01292786)))

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

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

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

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

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


rm(X_train_micro,Y_train_micro,C_train_micro,cox_DKplsDR_fit)

[Package plsRcox version 1.7.7 Index]