Package 'plsRcox'

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

Arguments to be passed on to survival::coxph.

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

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).

The multivariate regression method to be used. See mvrCv for details.

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.

Should the survival function be plotted ?)

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

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

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.

Should some details be displayed ?

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

PLSR components.

Final Cox-model.

The PLSR model.

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

Arguments to be passed on to survival::coxph.

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

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.

character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See pls for details

Should the survival function be plotted ?)

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

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

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.

Should some details be displayed ?

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

PLSR components.

Final Cox-model.

The PLSR model.

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

Arguments to be passed on to survival::coxph.

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

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.

character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See pls for details

Should the survival function be plotted ?)

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

Thresholding parameter. eta should be between 0 and 1.

Print out the progress of variable selection?

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

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.

Should some details be displayed ?

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

sPLSR components.

Final Cox-model.

The sPLSR model.

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

Arguments to be passed on to survival::coxph.

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

The number of components to include in the model. It this is not supplied, min(7,maximal number) components is used.

character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See pls for details

Should the survival function be plotted ?)

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

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

PLSR components.

Final Cox-model.

The PLSR model.

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

Arguments to be passed on to survival::coxph.

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

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).

The multivariate regression method to be used. See mvrCv for details.

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.

Should the survival function be plotted ?)

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

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

PLSR components.

Final Cox-model.

The PLSR model.

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

Arguments to be passed on to survival::coxph.

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

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).

The multivariate regression method to be used. See mvrCv for details.

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.

Should the survival function be plotted ?)

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

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

PLSR components.

Final Cox-model.

The PLSR model.

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

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

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

Number of PLSR components to fit.

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.

Should the survival function be plotted ?)

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

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

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

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

PLSR components.

Final Cox-model.

The PLSR model.

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

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

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

Number of PLSR components to fit.

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.

Should the survival function be plotted ?)

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

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

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

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

PLSR components.

Final Cox-model.

The PLSR model.

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

Arguments to be passed on to survival::coxph.

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

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.

character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See pls for details

Should the survival function be plotted ?)

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

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

PLSR components.

Final Cox-model.

The PLSR model.

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

Arguments to be passed on to survival::coxph.

for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.

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.

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.

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.

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.

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.

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.

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

Should the Xplan columns be standardized ?

Should the time values be standardized ?

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.

character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See pls for details

Should the survival function be plotted ?)

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

Thresholding parameter. eta should be between 0 and 1.

Print out the progress of variable selection?

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.

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

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

If TRUE, the model frame is returned.

If TRUE, the model matrix is returned.

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.

Final Cox-model.

sPLSR components.

Final Cox-model.

The sPLSR model.

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.

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.

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

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

Shall the results be displayed on a plot ?

Should standard errors be plotted ?

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

Shall the predictors be standardized ?

Should values and completion status for each folds be returned ?

Should the other 13 CV criteria be evaled and returned ?

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

Name to use to craft temporary results names

Should temporary results be saved ?

Should some CV details be displayed ?

Other arguments to pass to plsRcox.

The number of components requested

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

Optimal Nbr of components, max iAUC_CD criterion.

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

Optimal Nbr of components, max iAUC_hc criterion.

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

Optimal Nbr of components, max iAUC_sh criterion.

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

Optimal Nbr of components, max iAUC_Uno criterion.

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

Optimal Nbr of components, max iAUC_hz.train criterion.

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

Optimal Nbr of components, max iAUC_hz.test criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.train criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.test criterion.

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

Optimal Nbr of components, min iBrierScore unw criterion.

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

Optimal Nbr of components, min iSchmidScore unw criterion.

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

Optimal Nbr of components, min iBrierScore w criterion.

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

Optimal Nbr of components, min iSchmidScore w criterion.

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

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria

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

A list of three items:

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

• time passed to coxDKplsDR's time argument,

• status coxDKplsDR's status argument.

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.

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

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

Shall the results be displayed on a plot ?

Should standard errors be plotted ?

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

Shall the predictors be standardized ?

Should values and completion status for each folds be returned ?

Should the other 13 CV criteria be evaled and returned ?

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

Name to use to craft temporary results names

Should temporary results be saved ?

Should some CV details be displayed ?

Other arguments to pass to coxDKplsDR.

The number of components requested

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

Optimal Nbr of components, max iAUC_CD criterion.

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

Optimal Nbr of components, max iAUC_hc criterion.

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

Optimal Nbr of components, max iAUC_sh criterion.

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

Optimal Nbr of components, max iAUC_Uno criterion.

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

Optimal Nbr of components, max iAUC_hz.train criterion.

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

Optimal Nbr of components, max iAUC_hz.test criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.train criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.test criterion.

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

Optimal Nbr of components, min iBrierScore unw criterion.

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

Optimal Nbr of components, min iSchmidScore unw criterion.

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

Optimal Nbr of components, min iBrierScore w criterion.

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

Optimal Nbr of components, min iSchmidScore w criterion.

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

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria

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

A list of three items:

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

• time passed to coxDKsplsDR's time argument,

• status coxDKsplsDR's status argument.

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.

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

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

Thresholding parameter. eta should be between 0 and 1.

Shall the results be displayed on a plot ?

Should standard errors be plotted ?

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

Shall the predictors be standardized ?

Should the time values be standardized ?

Should values and completion status for each folds be returned ?

Should the other 13 CV criteria be evaled and returned ?

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

Name to use to craft temporary results names

Should temporary results be saved ?

Should some CV details be displayed ?

Other arguments to pass to coxDKsplsDR.

The number of components requested

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

Optimal Nbr of components, max iAUC_CD criterion.

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

Optimal Nbr of components, max iAUC_hc criterion.

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

Optimal Nbr of components, max iAUC_sh criterion.

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

Optimal Nbr of components, max iAUC_Uno criterion.

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

Optimal Nbr of components, max iAUC_hz.train criterion.

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

Optimal Nbr of components, max iAUC_hz.test criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.train criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.test criterion.

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

Optimal Nbr of components, min iBrierScore unw criterion.

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

Optimal Nbr of components, min iSchmidScore unw criterion.

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

Optimal Nbr of components, min iBrierScore w criterion.

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

Optimal Nbr of components, min iSchmidScore w criterion.

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

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria

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

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.

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.

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

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

Shall the results be displayed on a plot ?

Should standard errors be plotted ?

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

Shall the predictors be standardized ?

Should values and completion status for each folds be returned ?

Should the other 13 CV criteria be evaled and returned ?

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

Name to use to craft temporary results names

Should temporary results be saved ?

Should some CV details be displayed ?

Other arguments to pass to coxpls.

The number of components requested

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

Optimal Nbr of components, max iAUC_CD criterion.

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

Optimal Nbr of components, max iAUC_hc criterion.

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

Optimal Nbr of components, max iAUC_sh criterion.

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

Optimal Nbr of components, max iAUC_Uno criterion.

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

Optimal Nbr of components, max iAUC_hz.train criterion.

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

Optimal Nbr of components, max iAUC_hz.test criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.train criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.test criterion.

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

Optimal Nbr of components, min iBrierScore unw criterion.

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

Optimal Nbr of components, min iSchmidScore unw criterion.

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

Optimal Nbr of components, min iBrierScore w criterion.

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

Optimal Nbr of components, min iSchmidScore w criterion.

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

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria

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

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.

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.

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

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

Shall the results be displayed on a plot ?

Should standard errors be plotted ?

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

Shall the predictors be standardized ?

Should values and completion status for each folds be returned ?

Should the other 13 CV criteria be evaled and returned ?

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

Name to use to craft temporary results names

Should temporary results be saved ?

Should some CV details be displayed ?

Other arguments to pass to coxplsDR.

The number of components requested

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

Optimal Nbr of components, max iAUC_CD criterion.

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

Optimal Nbr of components, max iAUC_hc criterion.

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

Optimal Nbr of components, max iAUC_sh criterion.

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

Optimal Nbr of components, max iAUC_Uno criterion.

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

Optimal Nbr of components, max iAUC_hz.train criterion.

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

Optimal Nbr of components, max iAUC_hz.test criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.train criterion.

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

Optimal Nbr of components, max iAUC_survivalROC.test criterion.

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

Optimal Nbr of components, min iBrierScore unw criterion.

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

Optimal Nbr of components, min iSchmidScore unw criterion.

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

Optimal Nbr of components, min iBrierScore w criterion.

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

Optimal Nbr of components, min iSchmidScore w criterion.

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

If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria

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

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.

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.

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

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

Thresholding parameter. eta should be between 0 and 1.

Shall the results be displayed on a plot ?

Should standard errors be plotted ?

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

Shall the predictors be standardized ?

Should the time values be standardized ?

Should values and completion status for each folds be returned ?

Should the other 13 CV criteria be evaled and returned ?

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

Name to use to craft temporary results names

Should temporary results be saved ?

Should some CV details be displayed ?

Other arguments to pass to coxsplsDR.

The number of components requested

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

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.

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

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

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

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

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

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

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

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

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

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

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