Package 'plsRcox'

Title: Partial Least Squares Regression for Cox Models and Related Techniques
Description: Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
Authors: Frederic Bertrand [cre, aut] , Myriam Maumy-Bertrand [aut]
Maintainer: Frederic Bertrand <[email protected]>
License: GPL-3
Version: 1.7.7
Built: 2025-01-25 05:21:16 UTC
Source: https://github.com/fbertran/plsrcox

Help Index

plsRcox-package: Partial Least Squares Regression for Cox Models and Related Techniques

Description

Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.

References

Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. <doi:10.1093/bioinformatics/btu660>. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.

Examples

# The original allelotyping dataset

library(plsRcox)
data(micro.censure)
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]
Y_test_micro <- micro.censure$survyear[81:117]
C_test_micro <- micro.censure$DC[81:117]

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)

# coxsplsDR
cox_splsDR_fit=coxsplsDR(X_train_micro,Y_train_micro,C_train_micro,
ncomp=6,eta=.5)
cox_splsDR_fit
cox_splsDR_fit2=coxsplsDR(~X_train_micro,Y_train_micro,C_train_micro,
ncomp=6,eta=.5,trace=TRUE)
cox_splsDR_fit2
cox_splsDR_fit3=coxsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
dataXplan=X_train_micro_df,eta=.5)
cox_splsDR_fit3
rm(cox_splsDR_fit,cox_splsDR_fit2,cox_splsDR_fit3)

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

coxDKpls2DR(Xplan, ...)

## Default S3 method:
coxDKpls2DR(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  ncomp = min(7, ncol(Xplan)),
  methodpls = "kernelpls",
  validation = "CV",
  plot = FALSE,
  allres = FALSE,
  kernel = "rbfdot",
  hyperkernel,
  verbose = TRUE,
  ...
)

## S3 method for class 'formula'
coxDKpls2DR(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  ncomp = min(7, ncol(Xplan)),
  methodpls = "kernelpls",
  validation = "CV",
  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 (taking account of any cross-validation).
methodpls

The multivariate regression method to be used. See mvrCv for details.
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. See mvrCv for details. If validation = "LOO", leave-one-out cross-validation is performed. It is an error to specify the segments when validation = "LOO" is specified.
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 coxDKpls2DR 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_DKpls2DR

Final Cox-model.

If allres=TRUE :

tt_DKpls2DR

PLSR components.
cox_DKpls2DR

Final Cox-model.
DKpls2DR_mod

The PLSR model.

Author(s)

Frédéric Bertrand
[email protected]
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.

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_DKpls2DR_fit=coxDKpls2DR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV"))

#Fixing sigma to compare with pls2DR on Gram matrix; should be identical
(cox_DKpls2DR_fit=coxDKpls2DR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",hyperkernel=list(sigma=0.01292786)))

X_train_micro_kern <- kernlab::kernelMatrix(kernlab::rbfdot(sigma=0.01292786),scale(X_train_micro))
(cox_DKpls2DR_fit2=coxpls2DR(~X_train_micro_kern,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",scaleX=FALSE))

(cox_DKpls2DR_fit=coxDKpls2DR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",kernel="laplacedot",hyperkernel=list(sigma=0.01292786)))

X_train_micro_kern <- kernlab::kernelMatrix(kernlab::laplacedot(sigma=0.01292786),
scale(X_train_micro))
(cox_DKpls2DR_fit2=coxpls2DR(~X_train_micro_kern,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",scaleX=FALSE))

(cox_DKpls2DR_fit=coxDKpls2DR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV"))
(cox_DKpls2DR_fit=coxDKpls2DR(~.,Y_train_micro,C_train_micro,ncomp=6,validation="CV",
dataXplan=X_train_micro_df))

(cox_DKpls2DR_fit=coxDKpls2DR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",allres=TRUE))
(cox_DKpls2DR_fit=coxDKpls2DR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",allres=TRUE))
(cox_DKpls2DR_fit=coxDKpls2DR(~.,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_DKpls2DR_fit)

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
[email protected]
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.

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))

#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)

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

Description

This function computes the Cox Model based on sPLSR 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, the package spls to perform the first step in SPLSR then mixOmics to perform PLSR step fit.

Usage

coxDKsplsDR(Xplan, ...)

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

## S3 method for class 'formula'
coxDKsplsDR(
  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,
  trace = 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.
eta

Thresholding parameter. eta should be between 0 and 1.
trace

Print out the progress of variable selection?
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 coxDKsplsDR 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 sPLS components, the final Cox-model and the sPLSR model. allres=TRUE is useful for evluating model prediction accuracy on a test sample.

Value

If allres=FALSE :

cox_DKsplsDR

Final Cox-model.

If allres=TRUE :

tt_DKsplsDR

sPLSR components.
cox_DKsplsDR

Final Cox-model.
DKsplsDR_mod

The sPLSR model.

Author(s)

Frédéric Bertrand
[email protected]
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.

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_DKsplsDR_fit=coxDKsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",eta=.5))
(cox_DKsplsDR_fit=coxDKsplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",eta=.5))
(cox_DKsplsDR_fit=coxDKsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",dataXplan=data.frame(X_train_micro),eta=.5))

(cox_DKsplsDR_fit=coxDKsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",allres=TRUE,eta=.5))
(cox_DKsplsDR_fit=coxDKsplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",allres=TRUE,eta=.5))
(cox_DKsplsDR_fit=coxDKsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",allres=TRUE,dataXplan=data.frame(X_train_micro),eta=.5))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_DKsplsDR_fit)

Fitting a Cox-Model on PLSR components

Description

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

  • as the response: the Survival time

  • as explanatory variables: Xplan.

It uses the package mixOmics to perform PLSR fit.

Usage

coxpls(Xplan, ...)

## Default S3 method:
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 class '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,
  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. 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 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.
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 coxpls 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_pls

Final Cox-model.

If allres=TRUE :

tt_pls

PLSR components.
cox_pls

Final Cox-model.
pls_mod

The PLSR model.

Author(s)

Frédéric Bertrand
[email protected]
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.

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_pls_fit=coxpls(X_train_micro,Y_train_micro,C_train_micro,ncomp=6))
(cox_pls_fit=coxpls(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6))
(cox_pls_fit=coxpls(~.,Y_train_micro,C_train_micro,ncomp=6,dataXplan=X_train_micro_df))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls_fit)

Fitting a Cox-Model on PLSR components

Description

This function computes the the Cox-Model with PLSR components as the explanatory variables. It uses the package pls.

Usage

coxpls2(Xplan, ...)

## Default S3 method:
coxpls2(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  ncomp = min(7, ncol(Xplan)),
  methodpls = "kernelpls",
  validation = "CV",
  plot = FALSE,
  allres = FALSE,
  ...
)

## S3 method for class 'formula'
coxpls2(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  ncomp = min(7, ncol(Xplan)),
  methodpls = "kernelpls",
  validation = "CV",
  plot = FALSE,
  allres = FALSE,
  dataXplan = NULL,
  subset,
  weights,
  model_frame = FALSE,
  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 (taking account of any cross-validation).
methodpls

The multivariate regression method to be used. See mvrCv for details.
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. See mvrCv for details. If validation = "LOO", leave-one-out cross-validation is performed. It is an error to specify the segments when validation = "LOO" is specified.
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 taken from environment(Xplan), typically the environment from which coxpls2 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_pls

Final Cox-model.

If allres=TRUE :

tt_pls

PLSR components.
cox_pls

Final Cox-model.
pls_mod

The PLSR model.

Author(s)

Frédéric Bertrand
[email protected]
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.

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_pls_fit=coxpls2(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV"))
(cox_pls_fit=coxpls2(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="CV"))
(cox_pls_fit=coxpls2(~.,Y_train_micro,C_train_micro,ncomp=6,validation="CV",
dataXplan=X_train_micro_df))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls_fit)

Fitting a PLSR model on the (Deviance) Residuals

Description

This function computes the 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. It uses the package pls.

Usage

coxpls2DR(Xplan, ...)

## Default S3 method:
coxpls2DR(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  ncomp = min(7, ncol(Xplan)),
  methodpls = "kernelpls",
  validation = "CV",
  plot = FALSE,
  allres = FALSE,
  ...
)

## S3 method for class 'formula'
coxpls2DR(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  ncomp = min(7, ncol(Xplan)),
  methodpls = "kernelpls",
  validation = "CV",
  plot = FALSE,
  allres = FALSE,
  dataXplan = NULL,
  subset,
  weights,
  model_frame = FALSE,
  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 (taking account of any cross-validation).
methodpls

The multivariate regression method to be used. See mvrCv for details.
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. See mvrCv for details. If validation = "LOO", leave-one-out cross-validation is performed. It is an error to specify the segments when validation = "LOO" is specified.
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 taken from environment(Xplan), typically the environment from which coxpls2DR 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_pls2DR

Final Cox-model.

If allres=TRUE :

tt_pls2DR

PLSR components.
cox_pls2DR

Final Cox-model.
pls2DR_mod

The PLSR model.

Author(s)

Frédéric Bertrand
[email protected]
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.

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_pls2DR_fit=coxpls2DR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="none"))
(cox_pls2DR_fit2=coxpls2DR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="none"))
(cox_pls2DR_fit3=coxpls2DR(~.,Y_train_micro,C_train_micro,ncomp=6,validation="none",
dataXplan=X_train_micro_df))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls2DR_fit,cox_pls2DR_fit2,cox_pls2DR_fit3)

Fitting a Cox-Model on PLSR components

Description

This function computes the the Cox-Model with PLSR components as the explanatory variables. It uses the package plsRglm.

Usage

coxpls3(Xplan, ...)

## Default S3 method:
coxpls3(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(7, ncol(Xplan)),
  typeVC = "none",
  plot = FALSE,
  allres = FALSE,
  sparse = FALSE,
  sparseStop = TRUE,
  ...
)

## S3 method for class 'formula'
coxpls3(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(7, ncol(Xplan)),
  typeVC = "none",
  plot = FALSE,
  allres = FALSE,
  dataXplan = NULL,
  subset,
  weights,
  model_frame = FALSE,
  sparse = FALSE,
  sparseStop = 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 and to plsRglm::PLS_lm.
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 ?
nt

Number of PLSR components to fit.
typeVC

type of leave one out crossed validation. Several procedures are available and may be forced.

list("none")

no crossed validation

list("standard")

as in SIMCA for datasets without missing values and with all values predicted as those with missing values for datasets with any missing values

list("missingdata")

all values predicted as those with missing values for datasets with any missing values

list("adaptative")

predict a response value for an x with any missing value as those with missing values and for an x without any missing value as those without missing values.
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.
sparse

should the coefficients of non-significant predictors (<alpha.pvals.expli) be set to 0
sparseStop

should component extraction stop when no significant predictors (<alpha.pvals.expli) are found
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 coxpls3 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_pls3

Final Cox-model.

If allres=TRUE :

tt_pls3

PLSR components.
cox_pls3

Final Cox-model.
pls3_mod

The PLSR model.

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

coxph, PLS_lm

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_pls3_fit <- coxpls3(X_train_micro,Y_train_micro,C_train_micro,nt=7,typeVC="none"))
(cox_pls3_fit2 <- coxpls3(~X_train_micro,Y_train_micro,C_train_micro,nt=7,typeVC="none"))
(cox_pls3_fit3 <- coxpls3(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",data=X_train_micro_df))
(cox_pls3_fit4 <- coxpls3(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=TRUE))
(cox_pls3_fit5 <- coxpls3(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=FALSE,sparseStop=TRUE))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls3_fit,cox_pls3_fit2,
cox_pls3_fit3,cox_pls3_fit4,cox_pls3_fit5)

Fitting a PLSR model on the (Deviance) Residuals

Description

This function computes the 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. It uses the package plsRglm.

Usage

coxpls3DR(Xplan, ...)

## Default S3 method:
coxpls3DR(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(7, ncol(Xplan)),
  typeVC = "none",
  plot = FALSE,
  allres = FALSE,
  sparse = FALSE,
  sparseStop = TRUE,
  ...
)

## S3 method for class 'formula'
coxpls3DR(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(7, ncol(Xplan)),
  typeVC = "none",
  plot = FALSE,
  allres = FALSE,
  dataXplan = NULL,
  subset,
  weights,
  model_frame = FALSE,
  sparse = FALSE,
  sparseStop = 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 and to plsRglm::PLS_lm.
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 ?
nt

Number of PLSR components to fit.
typeVC

type of leave one out crossed validation. Several procedures are available and may be forced.

list("none")

no crossed validation

list("standard")

as in SIMCA for datasets without missing values and with all values predicted as those with missing values for datasets with any missing values

list("missingdata")

all values predicted as those with missing values for datasets with any missing values

list("adaptative")

predict a response value for an x with any missing value as those with missing values and for an x without any missing value as those without missing values.
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.
sparse

should the coefficients of non-significant predictors (<alpha.pvals.expli) be set to 0
sparseStop

should component extraction stop when no significant predictors (<alpha.pvals.expli) are found
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 coxpls3DR 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_pls3DR

Final Cox-model.

If allres=TRUE :

tt_pls3DR

PLSR components.
cox_pls3DR

Final Cox-model.
pls3DR_mod

The PLSR model.

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

coxph, PLS_lm

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_pls3DR_fit <- coxpls3DR(X_train_micro,Y_train_micro,C_train_micro,nt=7))
(cox_pls3DR_fit2 <- coxpls3DR(~X_train_micro,Y_train_micro,C_train_micro,nt=7))
(cox_pls3DR_fit3 <- coxpls3DR(~.,Y_train_micro,C_train_micro,nt=7,dataXplan=X_train_micro_df))
(cox_pls3DR_fit4 <- coxpls3DR(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=TRUE))
(cox_pls3DR_fit5 <- coxpls3DR(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=TRUE,sparseStop=FALSE))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls3DR_fit,cox_pls3DR_fit2,
cox_pls3DR_fit3,cox_pls3DR_fit4,cox_pls3DR_fit5)

Fitting a PLSR 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: Xplan.

It uses the package mixOmics to perform PLSR fit.

Usage

coxplsDR(Xplan, ...)

## Default S3 method:
coxplsDR(
  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 class 'formula'
coxplsDR(
  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,
  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.
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 coxplsDR 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_plsDR

Final Cox-model.

If allres=TRUE :

tt_plsDR

PLSR components.
cox_plsDR

Final Cox-model.
plsDR_mod

The PLSR model.

Author(s)

Frédéric Bertrand
[email protected]
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.

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_plsDR_fit=coxplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6))
(cox_plsDR_fit2=coxplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6))
(cox_plsDR_fit3=coxplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,dataXplan=X_train_micro_df))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_plsDR_fit,cox_plsDR_fit2,cox_plsDR_fit3)

Fitting a sPLSR model on the (Deviance) Residuals

Description

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

  • as the response: the Residuals of a Cox-Model 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.

Usage

coxsplsDR(Xplan, ...)

## Default S3 method:
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 class '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,
  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.
eta

Thresholding parameter. eta should be between 0 and 1.
trace

Print out the progress of variable selection?
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 coxsplsDR 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 sPLS components, the final Cox-model and the sPLSR model. allres=TRUE is useful for evluating model prediction accuracy on a test sample.

Value

If allres=FALSE :

cox_splsDR

Final Cox-model.

If allres=TRUE :

tt_splsDR

sPLSR components.
cox_splsDR

Final Cox-model.
splsDR_mod

The sPLSR model.

Author(s)

Frédéric Bertrand
[email protected]
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.

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_splsDR_fit=coxsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,eta=.5))
(cox_splsDR_fit2=coxsplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,eta=.5,trace=TRUE))
(cox_splsDR_fit3=coxsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
dataXplan=X_train_micro_df,eta=.5))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_splsDR_fit,cox_splsDR_fit2,cox_splsDR_fit3)

Cross-validating an autoplsRcox-Model

Description

This function cross-validates plsRcox models with automatic number of components selection.

Usage

cv.autoplsRcox(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  nt = 10,
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A list of three items:

  • x the explanatory variables passed to plsRcox's Xplan argument,

  • time passed to plsRcox's time argument,

  • status plsRcox's status argument.

method

A character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally.
nfold

The number of folds to use to perform the cross-validation process.
nt

The number of components to include in the model. It this is not supplied, 10 components are fitted.
plot.it

Shall the results be displayed on a plot ?
se

Should standard errors be plotted ?
givefold

Explicit list of omited values in each fold can be provided using this argument.
scaleX

Shall the predictors be standardized ?
folddetails

Should values and completion status for each folds be returned ?
allCVcrit

Should the other 13 CV criteria be evaled and returned ?
details

Should all results of the functions that perform error computations be returned ?
namedataset

Name to use to craft temporary results names
save

Should temporary results be saved ?
verbose

Should some CV details be displayed ?
...

Other arguments to pass to plsRcox.

Details

It only computes the recommended iAUCSH criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

Value

nt

The number of components requested
cv.error1

Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2

Vector with the mean values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error3

Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
cv.error4

Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
cv.error5

Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
cv.error6

Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.error7

Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.error8

Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.error9

Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.error10

Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.error11

Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.error12

Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.error13

Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
cv.error14

Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
cv.se1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se3

Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
cv.se4

Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
cv.se5

Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
cv.se6

Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.se7

Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.se8

Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.se9

Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.se10

Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.se11

Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.se12

Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.se13

Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
cv.se14

Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
folds

Explicit list of the values that were omited values in each fold.
lambda.min1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min1

Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1

Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2

Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2

Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
lambda.min3

Optimal Nbr of components, max iAUC_CD criterion.
lambda.se3

Optimal Nbr of components, max+1se iAUC_CD criterion.
lambda.min4

Optimal Nbr of components, max iAUC_hc criterion.
lambda.se4

Optimal Nbr of components, max+1se iAUC_hc criterion.
lambda.min5

Optimal Nbr of components, max iAUC_sh criterion.
lambda.se5

Optimal Nbr of components, max+1se iAUC_sh criterion.
lambda.min6

Optimal Nbr of components, max iAUC_Uno criterion.
lambda.se6

Optimal Nbr of components, max+1se iAUC_Uno criterion.
lambda.min7

Optimal Nbr of components, max iAUC_hz.train criterion.
lambda.se7

Optimal Nbr of components, max+1se iAUC_hz.train criterion.
lambda.min8

Optimal Nbr of components, max iAUC_hz.test criterion.
lambda.se8

Optimal Nbr of components, max+1se iAUC_hz.test criterion.
lambda.min9

Optimal Nbr of components, max iAUC_survivalROC.train criterion.
lambda.se9

Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
lambda.min10

Optimal Nbr of components, max iAUC_survivalROC.test criterion.
lambda.se10

Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
lambda.min11

Optimal Nbr of components, min iBrierScore unw criterion.
lambda.se11

Optimal Nbr of components, min+1se iBrierScore unw criterion.
lambda.min12

Optimal Nbr of components, min iSchmidScore unw criterion.
lambda.se12

Optimal Nbr of components, min+1se iSchmidScore unw criterion.
lambda.min13

Optimal Nbr of components, min iBrierScore w criterion.
lambda.se13

Optimal Nbr of components, min+1se iBrierScore w criterion.
lambda.min14

Optimal Nbr of components, min iSchmidScore w criterion.
lambda.se14

Optimal Nbr of components, min+1se iSchmidScore w criterion.
errormat1-14

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14

If details=TRUE, matrices with logical values for every folds across each of the components and each of the criteria: TRUE if the computation was completed and FALSE it is failed.
All_indics

All results of the functions that perform error computation, for each fold, each component and error criterion.

Author(s)

Frédéric Bertrand
[email protected]
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.

Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), https://arxiv.org/abs/1810.01005.

See Also

See Also plsRcox

Examples

data(micro.censure)
data(Xmicro.censure_compl_imp)
set.seed(123456)
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]

#Should be run with a higher value of nt (at least 10)
(cv.autoplsRcox.res=cv.autoplsRcox(list(x=X_train_micro,time=Y_train_micro,
status=C_train_micro),nt=3,verbose=FALSE))

Cross-validating a DKplsDR-Model

Description

This function cross-validates coxDKplsDR models.

Usage

cv.coxDKplsDR(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  nt = 10,
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A list of three items:

method

A character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally.
nfold

The number of folds to use to perform the cross-validation process.
nt

The number of components to include in the model. It this is not supplied, 10 components are fitted.
plot.it

Shall the results be displayed on a plot ?
se

Should standard errors be plotted ?
givefold

Explicit list of omited values in each fold can be provided using this argument.
scaleX

Shall the predictors be standardized ?
folddetails

Should values and completion status for each folds be returned ?
allCVcrit

Should the other 13 CV criteria be evaled and returned ?
details

Should all results of the functions that perform error computations be returned ?
namedataset

Name to use to craft temporary results names
save

Should temporary results be saved ?
verbose

Should some CV details be displayed ?
...

Other arguments to pass to coxDKplsDR.

Details

It only computes the recommended iAUCSurvROC criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

Value

nt

The number of components requested
cv.error1

Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2

Vector with the mean values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error3

Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
cv.error4

Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
cv.error5

Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
cv.error6

Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.error7

Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.error8

Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.error9

Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.error10

Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.error11

Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.error12

Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.error13

Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
cv.error14

Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
cv.se1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se3

Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
cv.se4

Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
cv.se5

Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
cv.se6

Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.se7

Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.se8

Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.se9

Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.se10

Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.se11

Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.se12

Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.se13

Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
cv.se14

Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
folds

Explicit list of the values that were omited values in each fold.
lambda.min1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min1

Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1

Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2

Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2

Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
lambda.min3

Optimal Nbr of components, max iAUC_CD criterion.
lambda.se3

Optimal Nbr of components, max+1se iAUC_CD criterion.
lambda.min4

Optimal Nbr of components, max iAUC_hc criterion.
lambda.se4

Optimal Nbr of components, max+1se iAUC_hc criterion.
lambda.min5

Optimal Nbr of components, max iAUC_sh criterion.
lambda.se5

Optimal Nbr of components, max+1se iAUC_sh criterion.
lambda.min6

Optimal Nbr of components, max iAUC_Uno criterion.
lambda.se6

Optimal Nbr of components, max+1se iAUC_Uno criterion.
lambda.min7

Optimal Nbr of components, max iAUC_hz.train criterion.
lambda.se7

Optimal Nbr of components, max+1se iAUC_hz.train criterion.
lambda.min8

Optimal Nbr of components, max iAUC_hz.test criterion.
lambda.se8

Optimal Nbr of components, max+1se iAUC_hz.test criterion.
lambda.min9

Optimal Nbr of components, max iAUC_survivalROC.train criterion.
lambda.se9

Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
lambda.min10

Optimal Nbr of components, max iAUC_survivalROC.test criterion.
lambda.se10

Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
lambda.min11

Optimal Nbr of components, min iBrierScore unw criterion.
lambda.se11

Optimal Nbr of components, min+1se iBrierScore unw criterion.
lambda.min12

Optimal Nbr of components, min iSchmidScore unw criterion.
lambda.se12

Optimal Nbr of components, min+1se iSchmidScore unw criterion.
lambda.min13

Optimal Nbr of components, min iBrierScore w criterion.
lambda.se13

Optimal Nbr of components, min+1se iBrierScore w criterion.
lambda.min14

Optimal Nbr of components, min iSchmidScore w criterion.
lambda.se14

Optimal Nbr of components, min+1se iSchmidScore w criterion.
errormat1-14

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14

If details=TRUE, matrices with logical values for every folds across each of the components and each of the criteria: TRUE if the computation was completed and FALSE it is failed.
All_indics

All results of the functions that perform error computation, for each fold, each component and error criterion.

Author(s)

Frédéric Bertrand
[email protected]
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.

Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), https://arxiv.org/abs/1810.01005.

See Also

See Also coxDKplsDR

Examples

data(micro.censure)
data(Xmicro.censure_compl_imp)
set.seed(123456)
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]

#Should be run with a higher value of nt (at least 10)
(cv.coxDKplsDR.res=cv.coxDKplsDR(list(x=X_train_micro,time=Y_train_micro,
status=C_train_micro),nt=3))

Cross-validating a DKsplsDR-Model

Description

This function cross-validates coxDKsplsDR models.

Usage

cv.coxDKsplsDR(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  nt = 10,
  eta = 0.5,
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  scaleY = FALSE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A list of three items:

method

A character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally.
nfold

The number of folds to use to perform the cross-validation process.
nt

The number of components to include in the model. It this is not supplied, 10 components are fitted.
eta

Thresholding parameter. eta should be between 0 and 1.
plot.it

Shall the results be displayed on a plot ?
se

Should standard errors be plotted ?
givefold

Explicit list of omited values in each fold can be provided using this argument.
scaleX

Shall the predictors be standardized ?
scaleY

Should the time values be standardized ?
folddetails

Should values and completion status for each folds be returned ?
allCVcrit

Should the other 13 CV criteria be evaled and returned ?
details

Should all results of the functions that perform error computations be returned ?
namedataset

Name to use to craft temporary results names
save

Should temporary results be saved ?
verbose

Should some CV details be displayed ?
...

Other arguments to pass to coxDKsplsDR.

Details

It only computes the recommended iAUCSurvROC criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

Value

nt

The number of components requested
cv.error1

Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2

Vector with the mean values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error3

Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
cv.error4

Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
cv.error5

Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
cv.error6

Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.error7

Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.error8

Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.error9

Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.error10

Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.error11

Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.error12

Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.error13

Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
cv.error14

Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
cv.se1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se3

Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
cv.se4

Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
cv.se5

Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
cv.se6

Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.se7

Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.se8

Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.se9

Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.se10

Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.se11

Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.se12

Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.se13

Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
cv.se14

Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
folds

Explicit list of the values that were omited values in each fold.
lambda.min1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min1

Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1

Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2

Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2

Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
lambda.min3

Optimal Nbr of components, max iAUC_CD criterion.
lambda.se3

Optimal Nbr of components, max+1se iAUC_CD criterion.
lambda.min4

Optimal Nbr of components, max iAUC_hc criterion.
lambda.se4

Optimal Nbr of components, max+1se iAUC_hc criterion.
lambda.min5

Optimal Nbr of components, max iAUC_sh criterion.
lambda.se5

Optimal Nbr of components, max+1se iAUC_sh criterion.
lambda.min6

Optimal Nbr of components, max iAUC_Uno criterion.
lambda.se6

Optimal Nbr of components, max+1se iAUC_Uno criterion.
lambda.min7

Optimal Nbr of components, max iAUC_hz.train criterion.
lambda.se7

Optimal Nbr of components, max+1se iAUC_hz.train criterion.
lambda.min8

Optimal Nbr of components, max iAUC_hz.test criterion.
lambda.se8

Optimal Nbr of components, max+1se iAUC_hz.test criterion.
lambda.min9

Optimal Nbr of components, max iAUC_survivalROC.train criterion.
lambda.se9

Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
lambda.min10

Optimal Nbr of components, max iAUC_survivalROC.test criterion.
lambda.se10

Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
lambda.min11

Optimal Nbr of components, min iBrierScore unw criterion.
lambda.se11

Optimal Nbr of components, min+1se iBrierScore unw criterion.
lambda.min12

Optimal Nbr of components, min iSchmidScore unw criterion.
lambda.se12

Optimal Nbr of components, min+1se iSchmidScore unw criterion.
lambda.min13

Optimal Nbr of components, min iBrierScore w criterion.
lambda.se13

Optimal Nbr of components, min+1se iBrierScore w criterion.
lambda.min14

Optimal Nbr of components, min iSchmidScore w criterion.
lambda.se14

Optimal Nbr of components, min+1se iSchmidScore w criterion.
errormat1-14

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14

If details=TRUE, matrices with logical values for every folds across each of the components and each of the criteria: TRUE if the computation was completed and FALSE it is failed.
All_indics

All results of the functions that perform error computation, for each fold, each component and error criterion.

Author(s)

Frédéric Bertrand
[email protected]
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.

Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), https://arxiv.org/abs/1810.01005.

See Also

See Also coxDKsplsDR

Examples

data(micro.censure)
data(Xmicro.censure_compl_imp)
set.seed(123456)
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]

#Should be run with a higher value of nt (at least 10) and a grid of eta
(cv.coxDKsplsDR.res=cv.coxDKsplsDR(list(x=X_train_micro,time=Y_train_micro,
status=C_train_micro),nt=3,eta=.1))

Cross-validating a Cox-Model fitted on PLSR components

Description

This function cross-validates coxpls models.

Usage

cv.coxpls(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  nt = 10,
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A list of three items:

  • x the explanatory variables passed to coxpls's Xplan argument,

  • time passed to coxpls's time argument,

  • status coxpls's status argument.

method

A character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally.
nfold

The number of folds to use to perform the cross-validation process.
nt

The number of components to include in the model. It this is not supplied, 10 components are fitted.
plot.it

Shall the results be displayed on a plot ?
se

Should standard errors be plotted ?
givefold

Explicit list of omited values in each fold can be provided using this argument.
scaleX

Shall the predictors be standardized ?
folddetails

Should values and completion status for each folds be returned ?
allCVcrit

Should the other 13 CV criteria be evaled and returned ?
details

Should all results of the functions that perform error computations be returned ?
namedataset

Name to use to craft temporary results names
save

Should temporary results be saved ?
verbose

Should some CV details be displayed ?
...

Other arguments to pass to coxpls.

Details

It only computes the recommended iAUCSurvROC criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

Value

nt

The number of components requested
cv.error1

Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2

Vector with the mean values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error3

Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
cv.error4

Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
cv.error5

Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
cv.error6

Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.error7

Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.error8

Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.error9

Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.error10

Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.error11

Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.error12

Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.error13

Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
cv.error14

Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
cv.se1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se3

Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
cv.se4

Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
cv.se5

Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
cv.se6

Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.se7

Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.se8

Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.se9

Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.se10

Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.se11

Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.se12

Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.se13

Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
cv.se14

Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
folds

Explicit list of the values that were omited values in each fold.
lambda.min1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min1

Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1

Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2

Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2

Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
lambda.min3

Optimal Nbr of components, max iAUC_CD criterion.
lambda.se3

Optimal Nbr of components, max+1se iAUC_CD criterion.
lambda.min4

Optimal Nbr of components, max iAUC_hc criterion.
lambda.se4

Optimal Nbr of components, max+1se iAUC_hc criterion.
lambda.min5

Optimal Nbr of components, max iAUC_sh criterion.
lambda.se5

Optimal Nbr of components, max+1se iAUC_sh criterion.
lambda.min6

Optimal Nbr of components, max iAUC_Uno criterion.
lambda.se6

Optimal Nbr of components, max+1se iAUC_Uno criterion.
lambda.min7

Optimal Nbr of components, max iAUC_hz.train criterion.
lambda.se7

Optimal Nbr of components, max+1se iAUC_hz.train criterion.
lambda.min8

Optimal Nbr of components, max iAUC_hz.test criterion.
lambda.se8

Optimal Nbr of components, max+1se iAUC_hz.test criterion.
lambda.min9

Optimal Nbr of components, max iAUC_survivalROC.train criterion.
lambda.se9

Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
lambda.min10

Optimal Nbr of components, max iAUC_survivalROC.test criterion.
lambda.se10

Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
lambda.min11

Optimal Nbr of components, min iBrierScore unw criterion.
lambda.se11

Optimal Nbr of components, min+1se iBrierScore unw criterion.
lambda.min12

Optimal Nbr of components, min iSchmidScore unw criterion.
lambda.se12

Optimal Nbr of components, min+1se iSchmidScore unw criterion.
lambda.min13

Optimal Nbr of components, min iBrierScore w criterion.
lambda.se13

Optimal Nbr of components, min+1se iBrierScore w criterion.
lambda.min14

Optimal Nbr of components, min iSchmidScore w criterion.
lambda.se14

Optimal Nbr of components, min+1se iSchmidScore w criterion.
errormat1-14

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14

If details=TRUE, matrices with logical values for every folds across each of the components and each of the criteria: TRUE if the computation was completed and FALSE it is failed.
All_indics

All results of the functions that perform error computation, for each fold, each component and error criterion.

Author(s)

Frédéric Bertrand
[email protected]
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.

Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), https://arxiv.org/abs/1810.01005.

See Also

See Also coxpls

Examples

data(micro.censure)
data(Xmicro.censure_compl_imp)
set.seed(123456)
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]

#Should be run with a higher value of nt (at least 10)
(cv.coxpls.res=cv.coxpls(list(x=X_train_micro,time=Y_train_micro,status=C_train_micro),nt=3))

Cross-validating a plsDR-Model

Description

This function cross-validates coxplsDR models.

Usage

cv.coxplsDR(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  nt = 10,
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A list of three items:

  • x the explanatory variables passed to coxplsDR's Xplan argument,

  • time passed to coxplsDR's time argument,

  • status coxplsDR's status argument.

method

A character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally.
nfold

The number of folds to use to perform the cross-validation process.
nt

The number of components to include in the model. It this is not supplied, 10 components are fitted.
plot.it

Shall the results be displayed on a plot ?
se

Should standard errors be plotted ?
givefold

Explicit list of omited values in each fold can be provided using this argument.
scaleX

Shall the predictors be standardized ?
folddetails

Should values and completion status for each folds be returned ?
allCVcrit

Should the other 13 CV criteria be evaled and returned ?
details

Should all results of the functions that perform error computations be returned ?
namedataset

Name to use to craft temporary results names
save

Should temporary results be saved ?
verbose

Should some CV details be displayed ?
...

Other arguments to pass to coxplsDR.

Details

It only computes the recommended iAUCSurvROC criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

Value

nt

The number of components requested
cv.error1

Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2

Vector with the mean values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error3

Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
cv.error4

Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
cv.error5

Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
cv.error6

Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.error7

Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.error8

Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.error9

Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.error10

Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.error11

Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.error12

Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.error13

Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
cv.error14

Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
cv.se1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se3

Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
cv.se4

Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
cv.se5

Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
cv.se6

Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.se7

Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.se8

Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.se9

Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.se10

Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.se11

Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.se12

Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.se13

Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
cv.se14

Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
folds

Explicit list of the values that were omited values in each fold.
lambda.min1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min1

Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1

Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2

Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2

Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
lambda.min3

Optimal Nbr of components, max iAUC_CD criterion.
lambda.se3

Optimal Nbr of components, max+1se iAUC_CD criterion.
lambda.min4

Optimal Nbr of components, max iAUC_hc criterion.
lambda.se4

Optimal Nbr of components, max+1se iAUC_hc criterion.
lambda.min5

Optimal Nbr of components, max iAUC_sh criterion.
lambda.se5

Optimal Nbr of components, max+1se iAUC_sh criterion.
lambda.min6

Optimal Nbr of components, max iAUC_Uno criterion.
lambda.se6

Optimal Nbr of components, max+1se iAUC_Uno criterion.
lambda.min7

Optimal Nbr of components, max iAUC_hz.train criterion.
lambda.se7

Optimal Nbr of components, max+1se iAUC_hz.train criterion.
lambda.min8

Optimal Nbr of components, max iAUC_hz.test criterion.
lambda.se8

Optimal Nbr of components, max+1se iAUC_hz.test criterion.
lambda.min9

Optimal Nbr of components, max iAUC_survivalROC.train criterion.
lambda.se9

Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
lambda.min10

Optimal Nbr of components, max iAUC_survivalROC.test criterion.
lambda.se10

Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
lambda.min11

Optimal Nbr of components, min iBrierScore unw criterion.
lambda.se11

Optimal Nbr of components, min+1se iBrierScore unw criterion.
lambda.min12

Optimal Nbr of components, min iSchmidScore unw criterion.
lambda.se12

Optimal Nbr of components, min+1se iSchmidScore unw criterion.
lambda.min13

Optimal Nbr of components, min iBrierScore w criterion.
lambda.se13

Optimal Nbr of components, min+1se iBrierScore w criterion.
lambda.min14

Optimal Nbr of components, min iSchmidScore w criterion.
lambda.se14

Optimal Nbr of components, min+1se iSchmidScore w criterion.
errormat1-14

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14

If details=TRUE, matrices with logical values for every folds across each of the components and each of the criteria: TRUE if the computation was completed and FALSE it is failed.
All_indics

All results of the functions that perform error computation, for each fold, each component and error criterion.

Author(s)

Frédéric Bertrand
[email protected]
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.

Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), https://arxiv.org/abs/1810.01005.

See Also

See Also coxplsDR

Examples

data(micro.censure)
data(Xmicro.censure_compl_imp)
set.seed(123456)
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]

#Should be run with a higher value of nt (at least 10)
(cv.coxplsDR.res=cv.coxplsDR(list(x=X_train_micro,time=Y_train_micro,status=C_train_micro),nt=3))

Cross-validating a splsDR-Model

Description

This function cross-validates coxsplsDR models.

Usage

cv.coxsplsDR(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  nt = 10,
  eta = 0.5,
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  scaleY = FALSE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A list of three items:

  • x the explanatory variables passed to coxsplsDR's Xplan argument,

  • time passed to coxsplsDR's time argument,

  • status coxsplsDR's status argument.

method

A character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally.
nfold

The number of folds to use to perform the cross-validation process.
nt

The number of components to include in the model. It this is not supplied, 10 components are fitted.
eta

Thresholding parameter. eta should be between 0 and 1.
plot.it

Shall the results be displayed on a plot ?
se

Should standard errors be plotted ?
givefold

Explicit list of omited values in each fold can be provided using this argument.
scaleX

Shall the predictors be standardized ?
scaleY

Should the time values be standardized ?
folddetails

Should values and completion status for each folds be returned ?
allCVcrit

Should the other 13 CV criteria be evaled and returned ?
details

Should all results of the functions that perform error computations be returned ?
namedataset

Name to use to craft temporary results names
save

Should temporary results be saved ?
verbose

Should some CV details be displayed ?
...

Other arguments to pass to coxsplsDR.

Details

It only computes the recommended iAUCSurvROC criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

Value

nt

The number of components requested
cv.error1

Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2

Vector with the mean values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error3

Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
cv.error4

Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
cv.error5

Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
cv.error6

Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.error7

Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.error8

Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.error9

Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.error10

Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.error11

Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.error12

Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.error13

Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
cv.error14

Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
cv.se1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se3

Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
cv.se4

Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
cv.se5

Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
cv.se6

Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.se7

Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.se8

Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.se9

Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.se10

Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.se11

Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.se12

Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.se13

Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
cv.se14

Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
folds

Explicit list of the values that were omited values in each fold.
lambda.min1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min1

Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1

Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2

Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2

Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
lambda.min3

Optimal Nbr of components, max iAUC_CD criterion.
lambda.se3

Optimal Nbr of components, max+1se iAUC_CD criterion.
lambda.min4

Optimal Nbr of components, max iAUC_hc criterion.
lambda.se4

Optimal Nbr of components, max+1se iAUC_hc criterion.
lambda.min5

Optimal Nbr of components, max iAUC_sh criterion.
lambda.se5

Optimal Nbr of components, max+1se iAUC_sh criterion.
lambda.min6

Optimal Nbr of components, max iAUC_Uno criterion.
lambda.se6

Optimal Nbr of components, max+1se iAUC_Uno criterion.
lambda.min7

Optimal Nbr of components, max iAUC_hz.train criterion.
lambda.se7

Optimal Nbr of components, max+1se iAUC_hz.train criterion.
lambda.min8

Optimal Nbr of components, max iAUC_hz.test criterion.
lambda.se8

Optimal Nbr of components, max+1se iAUC_hz.test criterion.
lambda.min9

Optimal Nbr of components, max iAUC_survivalROC.train criterion.
lambda.se9

Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
lambda.min10

Optimal Nbr of components, max iAUC_survivalROC.test criterion.
lambda.se10

Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
lambda.min11

Optimal Nbr of components, min iBrierScore unw criterion.
lambda.se11

Optimal Nbr of components, min+1se iBrierScore unw criterion.
lambda.min12

Optimal Nbr of components, min iSchmidScore unw criterion.
lambda.se12

Optimal Nbr of components, min+1se iSchmidScore unw criterion.
lambda.min13

Optimal Nbr of components, min iBrierScore w criterion.
lambda.se13

Optimal Nbr of components, min+1se iBrierScore w criterion.
lambda.min14

Optimal Nbr of components, min iSchmidScore w criterion.
lambda.se14

Optimal Nbr of components, min+1se iSchmidScore w criterion.
errormat1-14

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14

If details=TRUE, matrices with logical values for every folds across each of the components and each of the criteria: TRUE if the computation was completed and FALSE it is failed.
All_indics

All results of the functions that perform error computation, for each fold, each component and error criterion.

Author(s)

Frédéric Bertrand
[email protected]
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.

Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), https://arxiv.org/abs/1810.01005.

See Also

See Also coxsplsDR

Examples

data(micro.censure)
data(Xmicro.censure_compl_imp)
set.seed(123456)
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]

#Should be run with a higher value of nt (at least 10) and a grid of eta
(cv.coxsplsDR.res=cv.coxsplsDR(list(x=X_train_micro,time=Y_train_micro,
status=C_train_micro),nt=3,eta=.1))

Cross-validating a larsDR-Model

Description

This function cross-validates larsDR_coxph models.

Usage

cv.larsDR(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  fraction = seq(0, 1, length = 100),
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  scaleY = FALSE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A list of three items:

method

A character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally.
nfold

The number of folds to use to perform the cross-validation process.
fraction

L1 norm fraction.
plot.it

Shall the results be displayed on a plot ?
se

Should standard errors be plotted ?
givefold

Explicit list of omited values in each fold can be provided using this argument.
scaleX

Shall the predictors be standardized ?
scaleY

Should the time values be standardized ?
folddetails

Should values and completion status for each folds be returned ?
allCVcrit

Should the other 13 CV criteria be evaled and returned ?
details

Should all results of the functions that perform error computations be returned ?
namedataset

Name to use to craft temporary results names
save

Should temporary results be saved ?
verbose

Should some CV details be displayed ?
...

Other arguments to pass to larsDR_coxph.

Details

It only computes the recommended van Houwelingen CV partial likelihood criterion criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

Value

nt

The number of components requested
cv.error1

Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2

Vector with the mean values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error3

Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
cv.error4

Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
cv.error5

Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
cv.error6

Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.error7

Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.error8

Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.error9

Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.error10

Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.error11

Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.error12

Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.error13

Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
cv.error14

Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
cv.se1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se3

Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
cv.se4

Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
cv.se5

Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
cv.se6

Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.se7

Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.se8

Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.se9

Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.se10

Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.se11

Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.se12

Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.se13

Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
cv.se14

Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
folds

Explicit list of the values that were omited values in each fold.
lambda.min1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min1

Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1

Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2

Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2

Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
lambda.min3

Optimal Nbr of components, max iAUC_CD criterion.
lambda.se3

Optimal Nbr of components, max+1se iAUC_CD criterion.
lambda.min4

Optimal Nbr of components, max iAUC_hc criterion.
lambda.se4

Optimal Nbr of components, max+1se iAUC_hc criterion.
lambda.min5

Optimal Nbr of components, max iAUC_sh criterion.
lambda.se5

Optimal Nbr of components, max+1se iAUC_sh criterion.
lambda.min6

Optimal Nbr of components, max iAUC_Uno criterion.
lambda.se6

Optimal Nbr of components, max+1se iAUC_Uno criterion.
lambda.min7

Optimal Nbr of components, max iAUC_hz.train criterion.
lambda.se7

Optimal Nbr of components, max+1se iAUC_hz.train criterion.
lambda.min8

Optimal Nbr of components, max iAUC_hz.test criterion.
lambda.se8

Optimal Nbr of components, max+1se iAUC_hz.test criterion.
lambda.min9

Optimal Nbr of components, max iAUC_survivalROC.train criterion.
lambda.se9

Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
lambda.min10

Optimal Nbr of components, max iAUC_survivalROC.test criterion.
lambda.se10

Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
lambda.min11

Optimal Nbr of components, min iBrierScore unw criterion.
lambda.se11

Optimal Nbr of components, min+1se iBrierScore unw criterion.
lambda.min12

Optimal Nbr of components, min iSchmidScore unw criterion.
lambda.se12

Optimal Nbr of components, min+1se iSchmidScore unw criterion.
lambda.min13

Optimal Nbr of components, min iBrierScore w criterion.
lambda.se13

Optimal Nbr of components, min+1se iBrierScore w criterion.
lambda.min14

Optimal Nbr of components, min iSchmidScore w criterion.
lambda.se14

Optimal Nbr of components, min+1se iSchmidScore w criterion.
errormat1-14

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14

If details=TRUE, matrices with logical values for every folds across each of the components and each of the criteria: TRUE if the computation was completed and FALSE it is failed.
larsmodfull

Lars model fitted on the residuals.
All_indics

All results of the functions that perform error computation, for each fold, each component and error criterion.

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

See Also larsDR_coxph

Examples

data(micro.censure)
data(Xmicro.censure_compl_imp)
set.seed(123456)
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]

#Should be run with the default: fraction = seq(0, 1, length = 100)
(cv.larsDR.res=cv.larsDR(list(x=X_train_micro,time=Y_train_micro,
status=C_train_micro),se=TRUE,fraction=seq(0, 1, length = 4)))

Cross-validating a plsRcox-Model

Description

This function cross-validates plsRcox models.

Usage

cv.plsRcox(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  nt = 10,
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A list of three items:

  • x the explanatory variables passed to plsRcox's Xplan argument,

  • time passed to plsRcox's time argument,

  • status plsRcox's status argument.

method

A character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally.
nfold

The number of folds to use to perform the cross-validation process.
nt

The number of components to include in the model. It this is not supplied, 10 components are fitted.
plot.it

Shall the results be displayed on a plot ?
se

Should standard errors be plotted ?
givefold

Explicit list of omited values in each fold can be provided using this argument.
scaleX

Shall the predictors be standardized ?
folddetails

Should values and completion status for each folds be returned ?
allCVcrit

Should the other 13 CV criteria be evaled and returned ?
details

Should all results of the functions that perform error computations be returned ?
namedataset

Name to use to craft temporary results names
save

Should temporary results be saved ?
verbose

Should some CV details be displayed ?
...

Other arguments to pass to plsRcox.

Details

It only computes the recommended iAUCSH criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

Value

nt

The number of components requested
cv.error1

Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2

Vector with the mean values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error3

Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
cv.error4

Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
cv.error5

Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
cv.error6

Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.error7

Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.error8

Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.error9

Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.error10

Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.error11

Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.error12

Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.error13

Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
cv.error14

Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
cv.se1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se3

Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
cv.se4

Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
cv.se5

Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
cv.se6

Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.se7

Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.se8

Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.se9

Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.se10

Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.se11

Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.se12

Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.se13

Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
cv.se14

Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
folds

Explicit list of the values that were omited values in each fold.
lambda.min1

Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2

Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min1

Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1

Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2

Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2

Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
lambda.min3

Optimal Nbr of components, max iAUC_CD criterion.
lambda.se3

Optimal Nbr of components, max+1se iAUC_CD criterion.
lambda.min4

Optimal Nbr of components, max iAUC_hc criterion.
lambda.se4

Optimal Nbr of components, max+1se iAUC_hc criterion.
lambda.min5

Optimal Nbr of components, max iAUC_sh criterion.
lambda.se5

Optimal Nbr of components, max+1se iAUC_sh criterion.
lambda.min6

Optimal Nbr of components, max iAUC_Uno criterion.
lambda.se6

Optimal Nbr of components, max+1se iAUC_Uno criterion.
lambda.min7

Optimal Nbr of components, max iAUC_hz.train criterion.
lambda.se7

Optimal Nbr of components, max+1se iAUC_hz.train criterion.
lambda.min8

Optimal Nbr of components, max iAUC_hz.test criterion.
lambda.se8

Optimal Nbr of components, max+1se iAUC_hz.test criterion.
lambda.min9

Optimal Nbr of components, max iAUC_survivalROC.train criterion.
lambda.se9

Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
lambda.min10

Optimal Nbr of components, max iAUC_survivalROC.test criterion.
lambda.se10

Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
lambda.min11

Optimal Nbr of components, min iBrierScore unw criterion.
lambda.se11

Optimal Nbr of components, min+1se iBrierScore unw criterion.
lambda.min12

Optimal Nbr of components, min iSchmidScore unw criterion.
lambda.se12

Optimal Nbr of components, min+1se iSchmidScore unw criterion.
lambda.min13

Optimal Nbr of components, min iBrierScore w criterion.
lambda.se13

Optimal Nbr of components, min+1se iBrierScore w criterion.
lambda.min14

Optimal Nbr of components, min iSchmidScore w criterion.
lambda.se14

Optimal Nbr of components, min+1se iSchmidScore w criterion.
errormat1-14

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14

If details=TRUE, matrices with logical values for every folds across each of the components and each of the criteria: TRUE if the computation was completed and FALSE it is failed.
All_indics

All results of the functions that perform error computation, for each fold, each component and error criterion.

Author(s)

Frédéric Bertrand
[email protected]
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.

Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), https://arxiv.org/abs/1810.01005.

See Also

See Also plsRcox

Examples

data(micro.censure)
data(Xmicro.censure_compl_imp)
set.seed(123456)
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]

#Should be run with a higher value of nt (at least 10)
(cv.plsRcox.res=cv.plsRcox(list(x=X_train_micro,time=Y_train_micro,status=C_train_micro),nt=3))

Partial least squares Regression generalized linear models

Description

This function implements an extension of Partial least squares Regression to Cox Models.

Usage

DKplsRcox(Xplan, ...)

DKplsRcoxmodel(Xplan, ...)

## Default S3 method:
DKplsRcoxmodel(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(2, ncol(Xplan)),
  limQ2set = 0.0975,
  dataPredictY = Xplan,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  tol_Xi = 10^(-12),
  weights,
  control,
  sparse = FALSE,
  sparseStop = TRUE,
  plot = FALSE,
  allres = FALSE,
  kernel = "rbfdot",
  hyperkernel,
  verbose = TRUE,
  ...
)

## S3 method for class 'formula'
DKplsRcoxmodel(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = NULL,
  dataXplan = NULL,
  nt = min(2, ncol(Xplan)),
  limQ2set = 0.0975,
  dataPredictY = Xplan,
  pvals.expli = FALSE,
  model_frame = FALSE,
  alpha.pvals.expli = 0.05,
  tol_Xi = 10^(-12),
  weights,
  subset,
  control,
  sparse = FALSE,
  sparseStop = TRUE,
  plot = FALSE,
  allres = 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 pass to plsRmodel.default or to plsRmodel.formula
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 ?
nt

number of components to be extracted
limQ2set

limit value for the Q2
dataPredictY

predictor(s) (testing) dataset
pvals.expli

should individual p-values be reported to tune model selection ?
alpha.pvals.expli

level of significance for predictors when pvals.expli=TRUE
tol_Xi

minimal value for Norm2(Xi) and det(pp′×pp)\mathrm{det}(pp' \times pp) if there is any missing value in the dataX. It defaults to 10−1210^{-12}
weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
control

a list of parameters for controlling the fitting process. For glm.fit this is passed to glm.control.
sparse

should the coefficients of non-significant predictors (<alpha.pvals.expli) be set to 0
sparseStop

should component extraction stop when no significant predictors (<alpha.pvals.expli) are found
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.
model_frame

If TRUE, the model frame is returned.
subset

an optional vector specifying a subset of observations to be used in the fitting process.
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.
method

the method to be used in fitting the model. The default method "glm.fit" uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as glm.fit.

Details

A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.

A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula.

Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.

Value

Depends on the model that was used to fit the model.

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

plsR and plsRglm

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]


DKplsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
DKplsRcox(~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)



DKplsRcox(Xplan=X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, 
alpha.pvals.expli=.15)
DKplsRcox(Xplan=~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE,
alpha.pvals.expli=.15)

(Deviance) Residuals Computation

Description

This function computes the Residuals for a Cox-Model fitted with an intercept as the only explanatory variable. Default behaviour gives the Deviance residuals.

Usage

DR_coxph(
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleY = TRUE,
  plot = FALSE,
  ...
)

Arguments

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.
scaleY

Should the time values be standardized ?
plot

Should the survival function be plotted ?)
...

Arguments to be passed on to survival::coxph.

Value

Named num

Vector of the residual values.

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

coxph

Examples

data(micro.censure)
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]

DR_coxph(Y_train_micro,C_train_micro,plot=TRUE)
DR_coxph(Y_train_micro,C_train_micro,scaleY=FALSE,plot=TRUE)
DR_coxph(Y_train_micro,C_train_micro,scaleY=TRUE,plot=TRUE)

rm(Y_train_micro,C_train_micro)

Fitting a LASSO/LARS model on the (Deviance) Residuals

Description

This function computes the Cox Model based on lars variables computed model with

  • as the response: the Residuals of a Cox-Model fitted with no covariate

  • as explanatory variables: Xplan.

It uses the package lars to perform PLSR fit.

Usage

larsDR_coxph(Xplan, ...)

## Default S3 method:
larsDR_coxph(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = FALSE,
  scaleY = TRUE,
  plot = FALSE,
  typelars = "lasso",
  normalize = TRUE,
  max.steps,
  use.Gram = TRUE,
  allres = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'formula'
larsDR_coxph(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = FALSE,
  scaleY = TRUE,
  plot = FALSE,
  typelars = "lasso",
  normalize = TRUE,
  max.steps,
  use.Gram = TRUE,
  allres = FALSE,
  dataXplan = NULL,
  subset,
  weights,
  model_frame = FALSE,
  model_matrix = FALSE,
  verbose = TRUE,
  contrasts.arg = NULL,
  ...
)

Arguments

Xplan

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

Arguments to be passed on to survival::coxph or to lars::lars.
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 ?
plot

Should the survival function be plotted ?)
typelars

One of "lasso", "lar", "forward.stagewise" or "stepwise". The names can be abbreviated to any unique substring. Default is "lasso".
normalize

If TRUE, each variable is standardized to have unit L2 norm, otherwise it is left alone. Default is TRUE.
max.steps

Limit the number of steps taken; the default is 8 * min(m, n-intercept), with m the number of variables, and n the number of samples. For type="lar" or type="stepwise", the maximum number of steps is min(m,n-intercept). For type="lasso" and especially type="forward.stagewise", there can be many more terms, because although no more than min(m,n-intercept) variables can be active during any step, variables are frequently droppped and added as the algorithm proceeds. Although the default usually guarantees that the algorithm has proceeded to the saturated fit, users should check.
use.Gram

When the number m of variables is very large, i.e. larger than N, then you may not want LARS to precompute the Gram matrix. Default is use.Gram=TRUE
allres

FALSE to return only the Cox model and TRUE for additionnal results. See details. Defaults to FALSE.
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 plscox 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

This function computes the LASSO/LARS 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.

If allres=FALSE returns only the final Cox-model. If allres=TRUE returns a list with the (Deviance) Residuals, the LASSO/LARS model fitted to the (Deviance) Residuals, the eXplanatory variables and the final Cox-model. allres=TRUE is useful for evluating model prediction accuracy on a test sample.

Value

If allres=FALSE :

cox_larsDR

Final Cox-model.

If allres=TRUE :

DR_coxph

The (Deviance) Residuals.
larsDR

The LASSO/LARS model fitted to the (Deviance) Residuals.
X_larsDR

The eXplanatory variables.
cox_larsDR

Final Cox-model.

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

coxph, lars

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_larsDR_fit <- larsDR_coxph(X_train_micro,Y_train_micro,C_train_micro,max.steps=6,
use.Gram=FALSE,scaleX=TRUE))
(cox_larsDR_fit <- larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,
use.Gram=FALSE,scaleX=TRUE))
(cox_larsDR_fit <- larsDR_coxph(~.,Y_train_micro,C_train_micro,max.steps=6,
use.Gram=FALSE,scaleX=TRUE,dataXplan=X_train_micro_df))

larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,use.Gram=FALSE)
larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,use.Gram=FALSE,scaleX=FALSE)
larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,use.Gram=FALSE,
scaleX=TRUE,allres=TRUE)

rm(X_train_micro,Y_train_micro,C_train_micro,cox_larsDR_fit)

Microsat features and survival times

Description

This dataset provides Microsat specifications and survival times.

Format

A data frame with 117 observations on the following 43 variables.

numpat

a factor with levels B1006 B1017 B1028 B1031 B1046 B1059 B1068 B1071 B1102 B1115 B1124 B1139 B1157 B1161 B1164 B1188 B1190 B1192 B1203 B1211 B1221 B1225 B1226 B1227 B1237 B1251 B1258 B1266 B1271 B1282 B1284 B1285 B1286 B1287 B1290 B1292 B1298 B1302 B1304 B1310 B1319 B1327 B1353 B1357 B1363 B1368 B1372 B1373 B1379 B1388 B1392 B1397 B1403 B1418 B1421t1 B1421t2 B1448 B1451 B1455 B1460 B1462 B1466 B1469 B1493 B1500 B1502 B1519 B1523 B1529 B1530 B1544 B1548 B500 B532 B550 B558 B563 B582 B605 B609 B634 B652 B667 B679 B701 B722 B728 B731 B736 B739 B744 B766 B771 B777 B788 B800 B836 B838 B841 B848 B871 B873 B883 B889 B912 B924 B925 B927 B938 B952 B954 B955 B968 B972 B976 B982 B984

D18S61

a numeric vector

D17S794

a numeric vector

D13S173

a numeric vector

D20S107

a numeric vector

TP53

a numeric vector

D9S171

a numeric vector

D8S264

a numeric vector

D5S346

a numeric vector

D22S928

a numeric vector

D18S53

a numeric vector

D1S225

a numeric vector

D3S1282

a numeric vector

D15S127

a numeric vector

D1S305

a numeric vector

D1S207

a numeric vector

D2S138

a numeric vector

D16S422

a numeric vector

D9S179

a numeric vector

D10S191

a numeric vector

D4S394

a numeric vector

D1S197

a numeric vector

D6S264

a numeric vector

D14S65

a numeric vector

D17S790

a numeric vector

D5S430

a numeric vector

D3S1283

a numeric vector

D4S414

a numeric vector

D8S283

a numeric vector

D11S916

a numeric vector

D2S159

a numeric vector

D16S408

a numeric vector

D6S275

a numeric vector

D10S192

a numeric vector

sexe

a numeric vector

Agediag

a numeric vector

Siege

a numeric vector

T

a numeric vector

N

a numeric vector

M

a numeric vector

STADE

a factor with levels 0 1 2 3 4

survyear

a numeric vector

DC

a numeric vector

Source

Allelotyping identification of genomic alterations in rectal chromosomally unstable tumors without preoperative treatment, #' Benoît Romain, Agnès Neuville, Nicolas Meyer, Cécile Brigand, Serge Rohr, Anne Schneider, Marie-Pierre Gaub and Dominique Guenot, BMC Cancer 2010, 10:561, doi:10.1186/1471-2407-10-561.

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)
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]
Y_test_micro <- micro.censure$survyear[81:117]
C_test_micro <- micro.censure$DC[81:117]
rm(Y_train_micro,C_train_micro,Y_test_micro,C_test_micro)

Partial least squares Regression generalized linear models

Description

This function implements an extension of Partial least squares Regression to Cox Models.

Usage

plsRcox(Xplan, ...)

plsRcoxmodel(Xplan, ...)

## Default S3 method:
plsRcoxmodel(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(2, ncol(Xplan)),
  limQ2set = 0.0975,
  dataPredictY = Xplan,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  tol_Xi = 10^(-12),
  weights,
  control,
  sparse = FALSE,
  sparseStop = TRUE,
  allres = TRUE,
  verbose = TRUE,
  ...
)

## S3 method for class 'formula'
plsRcoxmodel(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = NULL,
  dataXplan = NULL,
  nt = min(2, ncol(Xplan)),
  limQ2set = 0.0975,
  dataPredictY = Xplan,
  pvals.expli = FALSE,
  model_frame = FALSE,
  alpha.pvals.expli = 0.05,
  tol_Xi = 10^(-12),
  weights,
  subset,
  control,
  sparse = FALSE,
  sparseStop = TRUE,
  allres = TRUE,
  verbose = TRUE,
  model_matrix = FALSE,
  contrasts.arg = NULL,
  ...
)

Arguments

Xplan

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

arguments to pass to plsRmodel.default or to plsRmodel.formula
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 ?
nt

number of components to be extracted
limQ2set

limit value for the Q2
dataPredictY

predictor(s) (testing) dataset
pvals.expli

should individual p-values be reported to tune model selection ?
alpha.pvals.expli

level of significance for predictors when pvals.expli=TRUE
tol_Xi

minimal value for Norm2(Xi) and det(pp′×pp)\mathrm{det}(pp' \times pp) if there is any missing value in the dataX. It defaults to 10−1210^{-12}
weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
control

a list of parameters for controlling the fitting process. For glm.fit this is passed to glm.control.
sparse

should the coefficients of non-significant predictors (<alpha.pvals.expli) be set to 0
sparseStop

should component extraction stop when no significant predictors (<alpha.pvals.expli) are found
allres

FALSE to return only the Cox model and TRUE for additionnal results. See details. Defaults to FALSE.
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.
model_frame

If TRUE, the model frame is returned.
subset

an optional vector specifying a subset of observations to be used in the fitting process.
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.
method

the method to be used in fitting the model. The default method "glm.fit" uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as glm.fit.

Details

A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.

A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula.

Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.

Value

Depends on the model that was used to fit the model.

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

plsR and plsRglm

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]

plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
plsRcox(~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)

plsRcox(Xplan=X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE,
alpha.pvals.expli=.15)
plsRcox(Xplan=~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE,
alpha.pvals.expli=.15)

Print method for plsRcox models

Description

This function provides a predict method for the class "plsRcoxmodel"

Usage

## S3 method for class 'plsRcoxmodel'
predict(
  object,
  newdata,
  comps = object$computed_nt,
  type = c("lp", "risk", "expected", "terms", "scores"),
  se.fit = FALSE,
  weights,
  methodNA = "adaptative",
  verbose = TRUE,
  ...
)

Arguments

object

An object of the class "plsRcoxmodel".
newdata

An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
comps

A value with a single value of component to use for prediction.
type

Type of predicted value. Choices are the linear predictor ("lp"), the risk score exp(lp) ("risk"), the expected number of events given the covariates and follow-up time ("expected"), the terms of the linear predictor ("terms") or the scores ("scores").
se.fit

If TRUE, pointwise standard errors are produced for the predictions using the Cox model.
weights

Vector of case weights. If weights is a vector of integers, then the estimated coefficients are equivalent to estimating the model from data with the individual cases replicated as many times as indicated by weights.
methodNA

Selects the way of predicting the response or the scores of the new data. For complete rows, without any missing value, there are two different ways of computing the prediction. As a consequence, for mixed datasets, with complete and incomplete rows, there are two ways of computing prediction : either predicts any row as if there were missing values in it (missingdata) or selects the prediction method accordingly to the completeness of the row (adaptative).
verbose

Should some details be displayed ?
...

Arguments to be passed on to survival::coxph and to plsRglm::PLS_lm.

Value

When type is "response", a matrix of predicted response values is returned.
When type is "scores", a score matrix is returned.

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

predict.coxph

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,]
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]

modpls <- plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=3)

predict(modpls)    
#Identical to predict(modpls,type="lp")    

predict(modpls,type="risk")    
predict(modpls,type="expected")    
predict(modpls,type="terms")    
predict(modpls,type="scores")    

predict(modpls,se.fit=TRUE)    
#Identical to predict(modpls,type="lp")    
predict(modpls,type="risk",se.fit=TRUE)    
predict(modpls,type="expected",se.fit=TRUE)    
predict(modpls,type="terms",se.fit=TRUE)    
predict(modpls,type="scores",se.fit=TRUE)    


#Identical to predict(modpls,type="lp")    
predict(modpls,newdata=X_train_micro[1:5,],type="risk")    
#predict(modpls,newdata=X_train_micro[1:5,],type="expected")    
predict(modpls,newdata=X_train_micro[1:5,],type="terms")    
predict(modpls,newdata=X_train_micro[1:5,],type="scores")    

#Identical to predict(modpls,type="lp")    
predict(modpls,newdata=X_train_micro[1:5,],type="risk",se.fit=TRUE)    
#predict(modpls,newdata=X_train_micro[1:5,],type="expected",se.fit=TRUE)    
predict(modpls,newdata=X_train_micro[1:5,],type="terms",se.fit=TRUE)    
predict(modpls,newdata=X_train_micro[1:5,],type="scores")    

predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=1)    
predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=2)    
predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=3)    
try(predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=4))

predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=1)    
predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=2)    
predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=3)    
try(predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=4))

predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=1)    
predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=2)    
predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=3)    
try(predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=4))

Print method for plsRcox models

Description

This function provides a print method for the class "plsRcoxmodel"

Usage

## S3 method for class 'plsRcoxmodel'
print(x, ...)

Arguments

x

an object of the class "plsRcoxmodel"
...

not used

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

print

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,]
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]

modpls <- plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=3)
print(modpls)

Print method for summaries of plsRcox models

Description

This function provides a print method for the class "summary.plsRcoxmodel"

Usage

## S3 method for class 'summary.plsRcoxmodel'
print(x, ...)

Arguments

x

an object of the class "summary.plsRcoxmodel"
...

not used

Value

language

call of the model

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

print and summary

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,]
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]

modpls <- plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=3)
print(summary(modpls))

Summary method for plsRcox models

Description

This function provides a summary method for the class "plsRcoxmodel"

Usage

## S3 method for class 'plsRcoxmodel'
summary(object, ...)

Arguments

object

an object of the class "plsRcoxmodel"
...

further arguments to be passed to or from methods.

Value

call

function call of plsRcox models

Author(s)

Frédéric Bertrand
[email protected]
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.

See Also

summary

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,]
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]

modpls <- plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=3)
summary(modpls)

Imputed Microsat features

Description

This dataset provides imputed microsat specifications. Imputations were computed using Multivariate Imputation by Chained Equations (MICE) using predictive mean matching for the numeric columns, logistic regression imputation for the binary data or the factors with 2 levels and polytomous regression imputation for categorical data i.e. factors with three or more levels.

Format

A data frame with 117 observations on the following 40 variables.

D18S61

a numeric vector

D17S794

a numeric vector

D13S173

a numeric vector

D20S107

a numeric vector

TP53

a numeric vector

D9S171

a numeric vector

D8S264

a numeric vector

D5S346

a numeric vector

D22S928

a numeric vector

D18S53

a numeric vector

D1S225

a numeric vector

D3S1282

a numeric vector

D15S127

a numeric vector

D1S305

a numeric vector

D1S207

a numeric vector

D2S138

a numeric vector

D16S422

a numeric vector

D9S179

a numeric vector

D10S191

a numeric vector

D4S394

a numeric vector

D1S197

a numeric vector

D6S264

a numeric vector

D14S65

a numeric vector

D17S790

a numeric vector

D5S430

a numeric vector

D3S1283

a numeric vector

D4S414

a numeric vector

D8S283

a numeric vector

D11S916

a numeric vector

D2S159

a numeric vector

D16S408

a numeric vector

D6S275

a numeric vector

D10S192

a numeric vector

sexe

a numeric vector

Agediag

a numeric vector

Siege

a numeric vector

T

a numeric vector

N

a numeric vector

M

a numeric vector

STADE

a factor with levels 0 1 2 3 4

Source

Allelotyping identification of genomic alterations in rectal chromosomally unstable tumors without preoperative treatment, Benoît Romain, Agnès Neuville, Nicolas Meyer, Cécile Brigand, Serge Rohr, Anne Schneider, Marie-Pierre Gaub and Dominique Guenot, BMC Cancer 2010, 10:561, doi:10.1186/1471-2407-10-561.

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(Xmicro.censure_compl_imp)
X_train_micro <- Xmicro.censure_compl_imp[1:80,]
X_test_micro <- Xmicro.censure_compl_imp[81:117,]
rm(X_train_micro,X_test_micro)