Package 'CoxBoost'

Title: Cox Models by Likelihood Based Boosting for a Single Survival Endpoint or Competing Risks
Description: Provides routines for fitting Cox models by likelihood based boosting for a single endpoint or in presence of competing risks.
Authors: Harald Binder <[email protected]>
Maintainer: Harald Binder <[email protected]>
License: GPL-2
Version: 1.5
Built: 2024-09-04 04:36:20 UTC
Source: https://github.com/binderh/CoxBoost

Help Index

Coeffients from CoxBoost fit

Description

Extracts the coefficients from the specified boosting steps of a CoxBoost object fitted by CoxBoost.

Usage

## S3 method for class 'CoxBoost'
coef(object, at.step = NULL, scaled = TRUE, ...)

Arguments

object

fitted CoxBoost object from a CoxBoost call.
at.step

scalar or vector of boosting step(s) at which prediction is wanted. If no step is given, the final boosting step is used.
scaled

logical value indicating whether coefficients should be returned scaled to be at the level of the original covariate values, or unscaled as used internally when standardize=TRUE is used in the CoxBoost call.
...

miscellaneous arguments, none of which is used at the moment.

Value

For a vector of length p (number of covariates) (at.step being a scalar) or a length(at.step) * p matrix (at.step being a vector).

Author(s)

Harald Binder [email protected]

Fit a Cox model by likelihood based boosting

Description

CoxBoost is used to fit a Cox proportional hazards model by componentwise likelihood based boosting. It is especially suited for models with a large number of predictors and allows for mandatory covariates with unpenalized parameter estimates.

Usage

CoxBoost(
  time,
  status,
  x,
  unpen.index = NULL,
  standardize = TRUE,
  subset = 1:length(time),
  weights = NULL,
  stratum = NULL,
  stepno = 100,
  penalty = 9 * sum(status[subset] == 1),
  criterion = c("pscore", "score", "hpscore", "hscore"),
  cmprsk = c("sh", "csh", "ccsh"),
  coupled.strata = TRUE,
  stepsize.factor = 1,
  sf.scheme = c("sigmoid", "linear"),
  pendistmat = NULL,
  connected.index = NULL,
  x.is.01 = FALSE,
  return.score = TRUE,
  trace = FALSE
)

Arguments

time

vector of length n specifying the observed times.
status

censoring indicator, i.e., vector of length n with entries 0 for censored observations and 1 for uncensored observations. If this vector contains elements not equal to 0 or 1, these are taken to indicate events from a competing risk and a model for the subdistribution hazard with respect to event 1 is fitted (see e.g. Fine and Gray, 1999; Binder et al. 2009a).
x

n * p matrix of covariates.
unpen.index

vector of length p.unpen with indices of mandatory covariates, where parameter estimation should be performed unpenalized.
standardize

logical value indicating whether covariates should be standardized for estimation. This does not apply for mandatory covariates, i.e., these are not standardized.
subset

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

optional vector of length n, for specifying weights for the individual observations.
stratum

vector specifying different groups of individuals for a stratified Cox regression. In CoxBoost fit each group gets its own baseline hazard.
stepno

number of boosting steps (m).
penalty

penalty value for the update of an individual element of the parameter vector in each boosting step.
criterion

indicates the criterion to be used for selection in each boosting step. "pscore" corresponds to the penalized score statistics, "score" to the un-penalized score statistics. Different results will only be seen for un-standardized covariates ("pscore" will result in preferential selection of covariates with larger covariance), or if different penalties are used for different covariates. "hpscore" and "hscore" correspond to "pscore" and "score". However, a heuristic is used for evaluating only a subset of covariates in each boosting step, as described in Binder et al. (2011). This can considerably speed up computation, but may lead to different results.
cmprsk

type of competing risk, specific hazards or cause-specific
stepsize.factor

determines the step-size modification factor by which the natural step size of boosting steps should be changed after a covariate has been selected in a boosting step. The default (value 1) implies constant penalties, for a value < 1 the penalty for a covariate is increased after it has been selected in a boosting step, and for a value > 1 the penalty it is decreased. If pendistmat is given, penalty updates are only performed for covariates that have at least one connection to another covariate.
sf.scheme

scheme for changing step sizes (via stepsize.factor). "linear" corresponds to the scheme described in Binder and Schumacher (2009b), "sigmoid" employs a sigmoid shape.
pendistmat

connection matrix with entries ranging between 0 and 1, with entry (i,j) indicating the certainty of the connection between covariates i and j. According to this information penalty changes due to stepsize.factor < 1 are propagated, i.e., if entry (i,j) is non-zero, the penalty for covariate j is decreased after it has been increased for covariate i, after it has been selected in a boosting step. This matrix either has to have dimension (p - p.unpen) * (p - p.unpen) or the indicices of the p.connected connected covariates have to be given in connected.index, in which case the matrix has to have dimension p.connected * p.connected. Generally, sparse matices from package Matrix can be used to save memory.
connected.index

indices of the p.connected connected covariates, for which pendistmat provides the connection information for distributing changes in penalties. No overlap with unpen.index is allowed. If NULL, and a connection matrix is given, all covariates are assumed to be connected.
x.is.01

logical value indicating whether (the non-mandatory part of) x contains just values 0 and 1, i.e., binary covariates. If this is the case and indicated by this argument, computations are much faster.
return.score

logical value indicating whether the value of the score statistic (or penalized score statistic, depending on criterion), as evaluated in each boosting step for every covariate, should be returned. The corresponding element scoremat can become very large (and needs much memory) when the number of covariates and boosting steps is large.
trace

logical value indicating whether progress in estimation should be indicated by printing the name of the covariate updated.

Details

In contrast to gradient boosting (implemented e.g. in the glmboost routine in the R package mboost, using the CoxPH loss function), CoxBoost is not based on gradients of loss functions, but adapts the offset-based boosting approach from Tutz and Binder (2007) for estimating Cox proportional hazards models. In each boosting step the previous boosting steps are incorporated as an offset in penalized partial likelihood estimation, which is employed for obtain an update for one single parameter, i.e., one covariate, in every boosting step. This results in sparse fits similar to Lasso-like approaches, with many estimated coefficients being zero. The main model complexity parameter, which has to be selected (e.g. by cross-validation using cv.CoxBoost), is the number of boosting steps stepno. The penalty parameter penalty can be chosen rather coarsely, either by hand or using optimCoxBoostPenalty.

The advantage of the offset-based approach compared to gradient boosting is that the penalty structure is very flexible. In the present implementation this is used for allowing for unpenalized mandatory covariates, which receive a very fast coefficient build-up in the course of the boosting steps, while the other (optional) covariates are subjected to penalization. For example in a microarray setting, the (many) microarray features would be taken to be optional covariates, and the (few) potential clinical covariates would be taken to be mandatory, by including their indices in unpen.index.

If a group of correlated covariates has influence on the response, e.g. genes from the same pathway, componentwise boosting will often result in a non-zero estimate for only one member of this group. To avoid this, information on the connection between covariates can be provided in pendistmat. If then, in addition, a penalty updating scheme with stepsize.factor < 1 is chosen, connected covariates are more likely to be chosen in future boosting steps, if a directly connected covariate has been chosen in an earlier boosting step (see Binder and Schumacher, 2009b).

Value

CoxBoost returns an object of class CoxBoost.

n, p

number of observations and number of covariates.
stepno

number of boosting steps.
xnames

vector of length p containing the names of the covariates. This information is extracted from x or names following the scheme V1, V2, ...

are used.

coefficients

(stepno+1) * p matrix containing the coefficient estimates for the (standardized) optional covariates for boosting steps 0 to stepno. This will typically be a sparse matrix, built using package Matrix

.

scoremat

stepno * p matrix containing the value of the score statistic for each of the optional covariates before each boosting step.
meanx, sdx

vector of mean values and standard deviations used for standardizing the covariates.
unpen.index

indices of the mandatory covariates in the original covariate matrix x.
penalty

If stepsize.factor != 1, stepno * (p - p.unpen) matrix containing the penalties used for every boosting step and every penalized covariate, otherwise a vector containing the unchanged values of the penalty employed in each boosting step.
time

observed times given in the CoxBoost call.
status

censoring indicator given in the CoxBoost call.
event.times

vector with event times from the data given in the CoxBoost call.
linear.predictors

(stepno+1) * n matrix giving the linear predictor for boosting steps 0 to stepno and every observation.
Lambda

matrix with the Breslow estimate for the cumulative baseline hazard for boosting steps 0 to stepno for every event time.
logplik

partial log-likelihood of the fitted model in the final boosting step.

Author(s)

Written by Harald Binder [email protected].

References

Binder, H., Benner, A., Bullinger, L., and Schumacher, M. (2013). Tailoring sparse multivariable regression techniques for prognostic single-nucleotide polymorphism signatures. Statistics in Medicine, doi: 10.1002/sim.5490.

Binder, H., Allignol, A., Schumacher, M., and Beyersmann, J. (2009). Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics, 25:890-896.

Binder, H. and Schumacher, M. (2009). Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinformatics. 10:18.

Binder, H. and Schumacher, M. (2008). Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics. 9:14.

Tutz, G. and Binder, H. (2007) Boosting ridge regression. Computational Statistics \& Data Analysis, 51(12):6044-6059.

Fine, J. P. and Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association. 94:496-509.

See Also

predict.CoxBoost, cv.CoxBoost.

Examples

#   Generate some survival data with 10 informative covariates
n <- 200; p <- 100
beta <- c(rep(1,10),rep(0,p-10))
x <- matrix(rnorm(n*p),n,p)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
status <- ifelse(real.time <= cens.time,1,0)
obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)

#   Fit a Cox proportional hazards model by CoxBoost

cbfit <- CoxBoost(time=obs.time,status=status,x=x,stepno=100,penalty=100)
summary(cbfit)

#   ... with covariates 1 and 2 being mandatory

cbfit.mand <- CoxBoost(time=obs.time,status=status,x=x,unpen.index=c(1,2),
                       stepno=100,penalty=100)
summary(cbfit.mand)

Determines the optimal number of boosting steps by cross-validation

Description

Performs a K-fold cross-validation for CoxBoost in search for the optimal number of boosting steps.

Usage

cv.CoxBoost(
  time,
  status,
  x,
  subset = 1:length(time),
  weights = NULL,
  stratum = NULL,
  maxstepno = 100,
  K = 10,
  type = c("verweij", "naive"),
  cmprsk = c("sh", "csh", "ccsh"),
  parallel = FALSE,
  upload.x = TRUE,
  multicore = FALSE,
  folds = NULL,
  trace = FALSE,
  ...
)

Arguments

time

vector of length n specifying the observed times.
status

censoring indicator, i.e., vector of length n with entries 0 for censored observations and 1 for uncensored observations. If this vector contains elements not equal to 0 or 1, these are taken to indicate events from a competing risk and a model for the subdistribution hazard with respect to event 1 is fitted (see e.g. Fine and Gray, 1999).
x

n * p matrix of covariates.
subset

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

weights to be passed to predict
stratum

vector specifying different groups of individuals for a stratified Cox regression. In CoxBoost fit each group gets its own baseline hazard.
maxstepno

maximum number of boosting steps to evaluate, i.e, the returned “optimal” number of boosting steps will be in the range [0,maxstepno].
K

number of folds to be used for cross-validation. If K is larger or equal to the number of non-zero elements in status, leave-one-out cross-validation is performed.
type

way of calculating the partial likelihood contribution of the observation in the hold-out folds: "verweij" uses the more appropriate method described in Verweij and van Houwelingen (1996), "naive" uses the approach where the observations that are not in the hold-out folds are ignored (often found in other R packages).
cmprsk

type of competing risk, specific hazards or cause-specific
parallel

logical value indicating whether computations in the cross-validation folds should be performed in parallel on a compute cluster, using package snowfall. Parallelization is performed via the package snowfall and the initialization function of of this package, sfInit, should be called before calling cv.CoxBoost.
upload.x

logical value indicating whether x should/has to be uploaded to the compute cluster for parallel computation. Uploading this only once (using sfExport(x) from library snowfall) can save much time for large data sets.
multicore

indicates whether computations in the cross-validation folds should be performed in parallel, using package parallel. If TRUE, package parallel is employed using the default number of cores. A value larger than 1 is taken to be the number of cores that should be employed.
folds

if not NULL, this has to be a list of length K, each element being a vector of indices of fold elements. Useful for employing the same folds for repeated runs.
trace

logical value indicating whether progress in estimation should be indicated by printing the number of the cross-validation fold and the index of the covariate updated.
...

miscellaneous parameters for the calls to CoxBoost

Value

List with the following components:

mean.logplik

vector of length maxstepno+1 with the mean partial log-likelihood for boosting steps 0 to maxstepno
se.logplik

vector with standard error estimates for the mean partial log-likelihood criterion for each boosting step.
optimal.step

optimal boosting step number, i.e., with minimum mean partial log-likelihood.
folds

list of length K, where the elements are vectors of the indices of observations in the respective folds.

Author(s)

Harald Binder [email protected]

References

Verweij, P. J. M. and van Houwelingen, H. C. (1993). Cross-validation in survival analysis. Statistics in Medicine, 12(24):2305-2314.

See Also

CoxBoost, optimCoxBoostPenalty

Examples

## Not run: 
#   Generate some survival data with 10 informative covariates
n <- 200; p <- 100
beta <- c(rep(1,10),rep(0,p-10))
x <- matrix(rnorm(n*p),n,p)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
status <- ifelse(real.time <= cens.time,1,0)
obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)


#  10-fold cross-validation

cv.res <- cv.CoxBoost(time=obs.time,status=status,x=x,maxstepno=500,
                      K=10,type="verweij",penalty=100)

#   examine mean partial log-likelihood in the course of the boosting steps
plot(cv.res$mean.logplik)

#   Fit with optimal number of boosting steps

cbfit <- CoxBoost(time=obs.time,status=status,x=x,stepno=cv.res$optimal.step,
                  penalty=100)
summary(cbfit)


## End(Not run)

Control paramters for cross-validation in iCoxBoost

Description

This function allows to set the control parameters for cross-validation to be passed into a call to iCoxBoost.

Usage

cvcb.control(
  K = 10,
  type = c("verweij", "naive"),
  parallel = FALSE,
  upload.x = TRUE,
  multicore = FALSE,
  folds = NULL
)

Arguments

K

number of folds to be used for cross-validation. If K is larger or equal to the number of events in the data to be analyzed, leave-one-out cross-validation is performed.
type

way of calculating the partial likelihood contribution of the observation in the hold-out folds: "verweij" uses the more appropriate method described in Verweij and van Houwelingen (1996), "naive" uses the approach where the observations that are not in the hold-out folds are ignored (often found in other R packages).
parallel

logical value indicating whether computations in the cross-validation folds should be performed in parallel on a compute cluster, using package snowfall. Parallelization is performed via the package snowfall and the initialization function of of this package, sfInit, should be called before calling iCoxBoost.
upload.x

logical value indicating whether x should/has to be uploaded to the compute cluster for parallel computation. Uploading this only once (using sfExport(x) from library snowfall) can save much time for large data sets.
multicore

indicates whether computations in the cross-validation folds should be performed in parallel, using package parallel. If TRUE, package parallel is employed using the default number of cores. A value larger than 1 is taken to be the number of cores that should be employed.
folds

if not NULL, this has to be a list of length K, each element being a vector of indices of fold elements. Useful for employing the same folds for repeated runs.

Value

List with elements corresponding to the call arguments.

Author(s)

Written by Harald Binder [email protected].

References

Verweij, P. J. M. and van Houwelingen, H. C. (1993). Cross-validation in survival analysis. Statistics in Medicine, 12(24):2305-2314.

See Also

iCoxBoost, cv.CoxBoost

Estimate p-values for a model fitted by CoxBoost

Description

Performs permutation-based p-value estimation for the optional covariates in a fit from CoxBoost.

Usage

estimPVal(
  object,
  x,
  permute.n = 10,
  per.covariate = FALSE,
  parallel = FALSE,
  multicore = FALSE,
  trace = FALSE,
  ...
)

Arguments

object

fit object obtained from CoxBoost.
x

n * p matrix of covariates. This has to be the same that was used in the call to CoxBoost.
permute.n

number of permutations employed for obtaining a null distribution.
per.covariate

logical value indicating whether a separate null distribution should be considered for each covariate. A larger number of permutations will be needed if this is wanted.
parallel

logical value indicating whether computations for obtaining a null distribution via permutation should be performed in parallel on a compute cluster. Parallelization is performed via the package snowfall and the initialization function of of this package, sfInit, should be called before calling estimPVal.
multicore

indicates whether computations in the permuted data sets should be performed in parallel, using package parallel. If TRUE, package parallel is employed using the default number of cores. A value larger than 1 is taken to be the number of cores that should be employed.
trace

logical value indicating whether progress in estimation should be indicated by printing the number of the permutation that is currently being evaluated.
...

miscellaneous parameters for the calls to CoxBoost

Details

As p-value estimates are based on permutations, random numbers are drawn for determining permutation indices. Therfore, the results depend on the state of the random number generator. This can be used to explore the variability due to random variation and help to determine an adequate value for permute.n. A value of 100 should be sufficient, but this can be quite slow. If there is a considerable number of covariates, e.g., larger than 100, a much smaller number of permutations, e.g., 10, might already work well. The estimates might also be negatively affected, if only a small number of boosting steps (say <50) was employed for the original fit.

Value

Vector with p-value estimates, one value for each optional covariate specificed in the original call to CoxBoost.

Author(s)

Harald Binder [email protected]

References

Binder, H., Porzelius, C. and Schumacher, M. (2009). Rank-based p-values for sparse high-dimensional risk prediction models fitted by componentwise boosting. FDM-Preprint Nr. 101, University of Freiburg, Germany.

See Also

CoxBoost

Examples

## Not run: 
#   Generate some survival data with 10 informative covariates
n <- 200; p <- 100
beta <- c(rep(1,10),rep(0,p-10))
x <- matrix(rnorm(n*p),n,p)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
status <- ifelse(real.time <= cens.time,1,0)
obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)

#   Fit a Cox proportional hazards model by CoxBoost

cbfit <- CoxBoost(time=obs.time,status=status,x=x,stepno=100,
                  penalty=100)

#   estimate p-values

p1 <- estimPVal(cbfit,x,permute.n=10)

#   get a second vector of estimates for checking how large
#   random variation is

p2 <- estimPVal(cbfit,x,permute.n=10)

plot(p1,p2,xlim=c(0,1),ylim=c(0,1),xlab="permute 1",ylab="permute 2")

## End(Not run)

Interface for cross-validation and model fitting using a formula description

Description

Formula interface for fitting a Cox proportional hazards model by componentwise likelihood based boosting (via a call to CoxBoost), where cross-validation can be performed automatically for determining the number of boosting steps (via a call to cv.CoxBoost).

Usage

iCoxBoost(
  formula,
  data = NULL,
  weights = NULL,
  subset = NULL,
  mandatory = NULL,
  cause = 1,
  cmprsk = c("sh", "csh", "ccsh"),
  standardize = TRUE,
  stepno = 200,
  criterion = c("pscore", "score", "hpscore", "hscore"),
  nu = 0.1,
  stepsize.factor = 1,
  varlink = NULL,
  cv = cvcb.control(),
  trace = FALSE,
  ...
)

Arguments

formula

A formula describing the model to be fitted, similar to a call to coxph. The response must be a survival object, either as returned by Surv or Hist (in a competing risks application).
data

data frame containing the variables described in the formula.
weights

optional vector, for specifying weights for the individual observations.
subset

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

vector containing the names of the covariates whose effect is to be estimated un-regularized.
cause

cause of interest in a competing risks setting, when the response is specified by Hist (see e.g. Fine and Gray, 1999; Binder et al. 2009a).
cmprsk

type of competing risk, specific hazards or cause-specific
standardize

logical value indicating whether covariates should be standardized for estimation. This does not apply for mandatory covariates, i.e., these are not standardized.
stepno

maximum number of boosting steps to be evaluated when determining the number of boosting steps by cross-validation, otherwise the number of boosting seps itself.
criterion

indicates the criterion to be used for selection in each boosting step. "pscore" corresponds to the penalized score statistics, "score" to the un-penalized score statistics. Different results will only be seen for un-standardized covariates ("pscore" will result in preferential selection of covariates with larger covariance), or if different penalties are used for different covariates. "hpscore" and "hscore" correspond to "pscore" and "score". However, a heuristic is used for evaluating only a subset of covariates in each boosting step, as described in Binder et al. (2011). This can considerably speed up computation, but may lead to different results.
nu

(roughly) the fraction of the partial maximum likelihood estimate used for the update in each boosting step. This is converted into a penalty for the call to CoxBoost. Use smaller values, e.g., 0.01 when there is little information in the data, and larger values, such as 0.1, with much information or when the number of events is larger than the number of covariates. Note that the default for direct calls to CoxBoost corresponds to nu=0.1.
stepsize.factor

determines the step-size modification factor by which the natural step size of boosting steps should be changed after a covariate has been selected in a boosting step. The default (value 1) implies constant nu, for a value < 1 the value nu for a covariate is decreased after it has been selected in a boosting step, and for a value > 1 the value nu is increased. If pendistmat is given, updates of nu are only performed for covariates that have at least one connection to another covariate.
varlink

list for specifying links between covariates, used to re-distribute step sizes when stepsize.factor != 1. The list needs to contain at least two vectors, the first containing the name of the source covariates, the second containing the names of the corresponding target covariates, and a third (optional) vector containing weights between 0 and 1 (defaulting to 1). If nu is increased/descreased for one of the source covariates according to stepsize.factor, the nu for the corresponding target covariate is descreased/increased accordingly (multiplied by the weight). If formula contains interaction terms, als rules for these can be set up, using variable names such as V1:V2 for the interaction term between covariates V1 and V2.
cv

TRUE, for performing cross-validation, with default parameters, FALSE for not performing cross-validation, or list containing the parameters for cross-validation, as obtained from a call to cvcb.control.
trace

logical value indicating whether progress in estimation should be indicated by printing the name of the covariate updated.
...

miscellaneous arguments, passed to the call to cv.CoxBoost.

Details

In contrast to gradient boosting (implemented e.g. in the glmboost routine in the R package mboost, using the CoxPH loss function), CoxBoost is not based on gradients of loss functions, but adapts the offset-based boosting approach from Tutz and Binder (2007) for estimating Cox proportional hazards models. In each boosting step the previous boosting steps are incorporated as an offset in penalized partial likelihood estimation, which is employed for obtain an update for one single parameter, i.e., one covariate, in every boosting step. This results in sparse fits similar to Lasso-like approaches, with many estimated coefficients being zero. The main model complexity parameter, the number of boosting steps, is automatically selected by cross-validation using a call to cv.CoxBoost). Note that this will introduce random variation when repeatedly calling iCoxBoost, i.e. it is advised to set/save the random number generator state for reproducible results.

The advantage of the offset-based approach compared to gradient boosting is that the penalty structure is very flexible. In the present implementation this is used for allowing for unpenalized mandatory covariates, which receive a very fast coefficient build-up in the course of the boosting steps, while the other (optional) covariates are subjected to penalization. For example in a microarray setting, the (many) microarray features would be taken to be optional covariates, and the (few) potential clinical covariates would be taken to be mandatory, by including their names in mandatory.

If a group of correlated covariates has influence on the response, e.g. genes from the same pathway, componentwise boosting will often result in a non-zero estimate for only one member of this group. To avoid this, information on the connection between covariates can be provided in varlink. If then, in addition, a penalty updating scheme with stepsize.factor < 1 is chosen, connected covariates are more likely to be chosen in future boosting steps, if a directly connected covariate has been chosen in an earlier boosting step (see Binder and Schumacher, 2009b).

Value

iCoxBoost returns an object of class iCoxBoost, which also has class CoxBoost. In addition to the elements from CoxBoost it has the following elements:

call, formula, terms

call, formula and terms from the formula interface.
cause

cause of interest.
cv.res

result from cv.CoxBoost, if cross-validation has been performed.

Author(s)

Written by Harald Binder [email protected].

References

Binder, H., Benner, A., Bullinger, L., and Schumacher, M. (2013). Tailoring sparse multivariable regression techniques for prognostic single-nucleotide polymorphism signatures. Statistics in Medicine, doi: 10.1002/sim.5490.

Binder, H., Allignol, A., Schumacher, M., and Beyersmann, J. (2009). Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics, 25:890-896.

Binder, H. and Schumacher, M. (2009). Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinformatics. 10:18.

Binder, H. and Schumacher, M. (2008). Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics. 9:14.

Tutz, G. and Binder, H. (2007) Boosting ridge regression. Computational Statistics \& Data Analysis, 51(12):6044-6059.

Fine, J. P. and Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association. 94:496-509.

See Also

predict.iCoxBoost, CoxBoost, cv.CoxBoost.

Examples

#   Generate some survival data with 10 informative covariates
n <- 200; p <- 100
beta <- c(rep(1,2),rep(0,p-2))
x <- matrix(rnorm(n*p),n,p)
actual.data <- as.data.frame(x)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
actual.data$status <- ifelse(real.time <= cens.time,1,0)
actual.data$time <- ifelse(real.time <= cens.time,real.time,cens.time)

#   Fit a Cox proportional hazards model by iCoxBoost

cbfit <- iCoxBoost(Surv(time,status) ~ .,data=actual.data)
summary(cbfit)
plot(cbfit)

#   ... with covariates 1 and 2 being mandatory

cbfit.mand <- iCoxBoost(Surv(time,status) ~ .,data=actual.data,mandatory=c("V1"))
summary(cbfit.mand)
plot(cbfit.mand)

Coarse line search for adequate penalty parameter

Description

This routine helps in finding a penalty value that leads to an “optimal” number of boosting steps for CoxBoost, determined by cross-validation, that is not too small/in a specified range.

Usage

optimCoxBoostPenalty(
  time,
  status,
  x,
  minstepno = 50,
  maxstepno = 200,
  start.penalty = 9 * sum(status == 1),
  iter.max = 10,
  upper.margin = 0.05,
  parallel = FALSE,
  trace = FALSE,
  ...
)

Arguments

time

vector of length n specifying the observed times.
status

censoring indicator, i.e., vector of length n with entries 0 for censored observations and 1 for uncensored observations. If this vector contains elements not equal to 0 or 1, these are taken to indicate events from a competing risk and a model for the subdistribution hazard with respect to event 1 is fitted (see e.g. Fine and Gray, 1999).
x

n * p matrix of covariates.
minstepno, maxstepno

range of boosting steps in which the “optimal” number of boosting steps is wanted to be.
start.penalty

start value for the search for the appropriate penalty.
iter.max

maximum number of search iterations.
upper.margin

specifies the fraction of maxstepno which is used as an upper margin in which a cross-validation minimum is not taken to be one. This is necessary because of random fluctuations of cross-validated partial log-likelihood.
parallel

logical value indicating whether computations in the cross-validation folds should be performed in parallel on a compute cluster. Parallelization is performed via the package snowfall and the initialization function of of this package, sfInit, should be called before calling cv.CoxBoost.
trace

logical value indicating whether information on progress should be printed.
...

miscellaneous parameters for cv.CoxBoost.

Details

The penalty parameter for CoxBoost has to be chosen only very coarsely. In Tutz and Binder (2006) it is suggested for likelihood based boosting just to make sure, that the optimal number of boosting steps, according to some criterion such as cross-validation, is larger or equal to 50. With a smaller number of steps, boosting may become too “greedy” and show sub-optimal performance. This procedure uses a very coarse line search and so one should specify a rather large range of boosting steps.

Value

List with element penalty containing the “optimal” penalty and cv.res containing the corresponding result of cv.CoxBoost.

Author(s)

Written by Harald Binder [email protected].

References

Tutz, G. and Binder, H. (2006) Generalized additive modelling with implicit variable selection by likelihood based boosting. Biometrics, 62:961-971.

See Also

CoxBoost, cv.CoxBoost

Examples

## Not run: 
#   Generate some survival data with 10 informative covariates
n <- 200; p <- 100
beta <- c(rep(1,10),rep(0,p-10))
x <- matrix(rnorm(n*p),n,p)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
status <- ifelse(real.time <= cens.time,1,0)
obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)

#  determine penalty parameter

optim.res <- optimCoxBoostPenalty(time=obs.time,status=status,x=x,
                                  trace=TRUE,start.penalty=500)

#   Fit with obtained penalty parameter and optimal number of boosting
#   steps obtained by cross-validation

cbfit <- CoxBoost(time=obs.time,status=status,x=x,
                  stepno=optim.res$cv.res$optimal.step,
                  penalty=optim.res$penalty)
summary(cbfit)


## End(Not run)

Coarse line search for optimum step-size modification factor

Description

This routine helps in finding an optimum step-size modification factor for CoxBoost, i.e., that results in an optimum in terms of cross-validated partial log-likelihood.

Usage

optimStepSizeFactor(
  time,
  status,
  x,
  direction = c("down", "up", "both"),
  start.stepsize = 0.1,
  iter.max = 10,
  constant.cv.res = NULL,
  parallel = FALSE,
  trace = FALSE,
  ...
)

Arguments

time

vector of length n specifying the observed times.
status

censoring indicator, i.e., vector of length n with entries 0 for censored observations and 1 for uncensored observations. If this vector contains elements not equal to 0 or 1, these are taken to indicate events from a competing risk and a model for the subdistribution hazard with respect to event 1 is fitted (see e.g. Fine and Gray, 1999).
x

n * p matrix of covariates.
direction

direction of line search for an optimal step-size modification factor (starting from value 1).
start.stepsize

step size used for the line search. A final step is performed using half this size.
iter.max

maximum number of search iterations.
constant.cv.res

result of cv.CoxBoost for stepsize.factor=1, that can be provided for saving computing time, if it already is available.
parallel

logical value indicating whether computations in the cross-validation folds should be performed in parallel on a compute cluster. Parallelization is performed via the package snowfall and the initialization function of of this package, sfInit, should be called before calling cv.CoxBoost.
trace

logical value indicating whether information on progress should be printed.
...

miscellaneous parameters for cv.CoxBoost.

Details

A coarse line search is performed for finding the best parameter stepsize.factor for CoxBoost. If an pendistmat argument is provided (which is passed on to CoxBoost), a search for factors smaller than 1 is sensible (corresponding to direction="down"). If no connection information is provided, it is reasonable to employ direction="both", for avoiding restrictions without subject matter knowledge.

Value

List with the following components:

factor.list

array with the evaluated step-size modification factors.
critmat

matrix with the mean partial log-likelihood for each step-size modification factor in the course of the boosting steps.
optimal.factor.index

index of the optimal step-size modification factor.
optimal.factor

optimal step-size modification factor.
optimal.step

optimal boosting step number, i.e., with minimum mean partial log-likelihood, for step-size modification factor optimal.factor.

Author(s)

Written by Harald Binder [email protected].

References

Binder, H. and Schumacher, M. (2009). Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinformatics. 10:18.

See Also

CoxBoost, cv.CoxBoost

Examples

## Not run: 
#   Generate some survival data with 10 informative covariates
n <- 200; p <- 100
beta <- c(rep(1,10),rep(0,p-10))
x <- matrix(rnorm(n*p),n,p)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
status <- ifelse(real.time <= cens.time,1,0)
obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)

#  Determine step-size modification factor. As there is no connection matrix,
#  perform search into both directions

optim.res <- optimStepSizeFactor(direction="both",
                                time=obs.time,status=status,x=x,
                                trace=TRUE)

#   Fit with obtained step-size modification parameter and optimal number of boosting
#   steps obtained by cross-validation

cbfit <- CoxBoost(time=obs.time,status=status,x=x,
                  stepno=optim.res$optimal.step,
                  stepsize.factor=optim.res$optimal.factor)
summary(cbfit)


## End(Not run)

Plot coefficient paths from CoxBoost fit

Description

Plots coefficient paths, i.e. the parameter estimates plotted against the boosting steps as obtained from a CoxBoost object fitted by CoxBoost.

Usage

## S3 method for class 'CoxBoost'
plot(
  x,
  line.col = "dark grey",
  label.cex = 0.6,
  xlab = NULL,
  ylab = NULL,
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  ...
)

Arguments

x

fitted CoxBoost object from a CoxBoost call.
line.col

color of the lines of the coefficient path
label.cex

scaling factor for the variable labels.
xlab

label for the x axis, default label when NULL.
ylab

label for the y axis, default label when NULL.
xlim, ylim

plotting ranges, default label when NULL.
main

a main title for the plot
...

miscellaneous arguments, passed to the plot routine.

Value

No value is returned, but a plot is generated.

Author(s)

Harald Binder [email protected]

Predict method for CoxBoost fits

Description

Obtains predictions at specified boosting steps from a CoxBoost object fitted by CoxBoost.

Usage

## S3 method for class 'CoxBoost'
predict(
  object,
  newdata = NULL,
  newtime = NULL,
  newstatus = NULL,
  subset = NULL,
  weights = NULL,
  stratum = NULL,
  at.step = NULL,
  times = NULL,
  type = c("lp", "logplik", "risk", "CIF"),
  ...
)

Arguments

object

fitted CoxBoost object from a CoxBoost call.
newdata

n.new * p matrix with new covariate values. If just prediction for the training data is wanted, it can be omitted.
newtime, newstatus

vectors with observed time and censoring indicator (0 for censoring, 1 for no censoring, and any other values for competing events in a competing risks setting) for new observations, where prediction is wanted. Only required if predicted partial log-likelihood is wanted, i.e., if type="logplik". This can also be omitted when prediction is only wanted for the training data, i.e., newdata=NULL.
subset

an optional vector specifying a subset of observations to be used for evaluation.
weights

weights for each time.
stratum

vector specifying different groups of individuals for a stratified Cox regression. In CoxBoost fit each group gets its own baseline hazard.
at.step

scalar or vector of boosting step(s) at which prediction is wanted. If type="risk" is used, only one step is admissible. If no step is given, the final boosting step is used.
times

vector with T time points where prediction is wanted. Only needed for type="risk"
type

type of prediction to be returned: "lp" gives the linear predictor, "logplik" the partial log-likelihood, "risk" the predicted probability of not yet having had the event at the time points given in times, and "CIF" the predicted cumulative incidence function, i.e., the predicted probability of having had the event of interest.
...

miscellaneous arguments, none of which is used at the moment.

Value

For type="lp" and type="logplik" a vector of length n.new (at.step being a scalar) or a n.new * length(at.step) matrix (at.step being a vector) with predictions is returned. For type="risk" or type="CIF" a n.new * T matrix with predicted probabilities at the specific time points is returned.

Author(s)

Harald Binder [email protected]

Examples

#   Generate some survival data with 10 informative covariates
n <- 200; p <- 100
beta <- c(rep(1,10),rep(0,p-10))
x <- matrix(rnorm(n*p),n,p)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
status <- ifelse(real.time <= cens.time,1,0)
obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)

#   define training and test set

train.index <- 1:100
test.index <- 101:200

#   Fit CoxBoost to the training data

cbfit <- CoxBoost(time=obs.time[train.index],status=status[train.index],
                  x=x[train.index,],stepno=300,penalty=100)

#   mean partial log-likelihood for test set in every boosting step

step.logplik <- predict(cbfit,newdata=x[test.index,],
                        newtime=obs.time[test.index],
                        newstatus=status[test.index],
                        at.step=0:300,type="logplik")

plot(step.logplik)

#   names of covariates with non-zero coefficients at boosting step
#   with maximal test set partial log-likelihood

print(cbfit$xnames[cbfit$coefficients[which.max(step.logplik),] != 0])

Predict method for iCoxBoost fits

Description

Obtains predictions at specified boosting steps from a iCoxBoost object fitted by iCoxBoost.

Usage

## S3 method for class 'iCoxBoost'
predict(
  object,
  newdata = NULL,
  subset = NULL,
  at.step = NULL,
  times = NULL,
  type = c("lp", "logplik", "risk", "CIF"),
  ...
)

Arguments

object

fitted CoxBoost object from a CoxBoost call.
newdata

data frame with new covariate values (for n.new observations). If just prediction for the training data is wanted, it can be omitted. If the predictive partial log-likelihood is wanted (type=logplik), this frame also has to contain the response information.
subset

an optional vector specifying a subset of observations to be used for evaluation.
at.step

scalar or vector of boosting step(s) at which prediction is wanted. If type="risk" is used, only one step is admissible. If no step is given, the final boosting step is used.
times

vector with T time points where prediction is wanted. Only needed for type="risk"
type

type of prediction to be returned: "lp" gives the linear predictor, "logplik" the partial log-likelihood, "risk" the predicted probability of not yet having had the event at the time points given in times, and "CIF" the predicted cumulative incidence function, i.e., the predicted probability of having had the event of interest.
...

miscellaneous arguments, none of which is used at the moment.

Value

For type="lp" and type="logplik" a vector of length n.new (at.step being a scalar) or a n.new * length(at.step) matrix (at.step being a vector) with predictions is returned. For type="risk" or type="CIF" a n.new * T matrix with predicted probabilities at the specific time points is returned.

Author(s)

Harald Binder [email protected]

Examples

n <- 200; p <- 100
beta <- c(rep(1,2),rep(0,p-2))
x <- matrix(rnorm(n*p),n,p)
actual.data <- as.data.frame(x)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
actual.data$status <- ifelse(real.time <= cens.time,1,0)
actual.data$time <- ifelse(real.time <= cens.time,real.time,cens.time)

#   define training and test set

train.index <- 1:100
test.index <- 101:200

#   Fit a Cox proportional hazards model by iCoxBoost

cbfit <- iCoxBoost(Surv(time,status) ~ .,data=actual.data[train.index,],
				   stepno=300,cv=FALSE)

#   mean partial log-likelihood for test set in every boosting step

step.logplik <- predict(cbfit,newdata=actual.data[test.index,],
                        at.step=0:300,type="logplik")

plot(step.logplik)

#   names of covariates with non-zero coefficients at boosting step
#   with maximal test set partial log-likelihood

print(coef(cbfit,at.step=which.max(step.logplik)-1))

Print a CoxBoost type

Description

Print a CoxBoost type

Usage

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

Arguments

x

a CoxBoost object
...

not used

Performs resampling for a stratified weighted Cox model by componentwise likelihood based boosting for developing subgroup covariate signatures

Description

resample.CoxBoost is used to perform resampling for stratified Cox proportional hazards models by componentwise likelihood based boosting with weights for developing subgroup covariate signatures. Specifically, this weighted Cox regression approach is for performing a subgroup analysis, not for the standard subgroup analysis but for a weighted form of it where the observations of the not analyzed subgroup are down-weighted. resample.CoxBoost calls cv.CoxBoost and CoxBoost.

Usage

resample.CoxBoost(
  time,
  status,
  x,
  rep = 100,
  maxstepno = 200,
  multicore = TRUE,
  mix.list = c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99),
  stratum,
  stratnotinfocus = 0,
  penalty = sum(status) * (1/0.02 - 1),
  criterion = "hscore",
  unpen.index = NULL
)

Arguments

time

vector of length n with the observed times of each individual.
status

vector of length n with entries 0 for censored observations and 1 for observations having an event.
x

n * p matrix of mandatory and optional covariates.
rep

number of resampling data sets used for the evaluation of model stability. Should be equal to length(iter).
maxstepno

maximum number of boosting steps needed for cross-validation in cv.CoxBoost. So the optimal step number for fitting the compontentwise boosting has a range between zero and maxstepno.
multicore

logical value: If TRUE then the cross-validation for selecting the number of optimal boosting steps within each resampling data set is been parallelized, if FALSE there will be no parallelization.
mix.list

vector of different relative weights between zero and one, for specifying weights for the individual observations for the weighted regression approach. For each weight, a stratified Cox regression by componentwise boosting is fitted.
stratum

vector specifying different groups of individuals for a stratified Cox regression. In CoxBoost fit each group gets its own baseline hazard.
stratnotinfocus

value for the group which is not analyzed but should be weighted down in the analysis with the different weights element of mix.list.
penalty

penalty value for the covariates for the update of an individual element in each boosting step. The mandatory covariates should get a penalty value of zero, which can be realized with unpen.index.
criterion

indicates the criterion to be used for selection in each boosting step in CoxBoost. The default is "hscore" for using a heuristic when evaluating a subset of covariates in each boosting step. For the different other options see CoxBoost.
unpen.index

index for mandatory covariates, which should get no penalty value in the log-partial likelihood function.

Details

The CoxBoost function can be used for performing variable selection for time-to-event data based on componentwise likelihood based boosting (Binder and Schumacher, 2009; Binder et al., 2013) for obtaining signatures for high dimensional data, such as gene expression or high throughput methylation measurements. If there is any heterogeneity due to known patient subgroups, it would be interesting for developing subgroup signatures to account for this heterogeneity. To account for heterogeneity in the data a stratified Cox model via stratum can be performed, which allows for different baseline hazards in each subgroup. For performing a stratified CoxBoost for such heterogeneous subgroups the stratum can be set to a subgroup variable, containing also the subgroup for which a subgroup signature should be developed. A standard subgroup analysis may lead to a decreased statistical power. With a weighted approach based on componentwise likelihood-based boosting one can develop subgroup signatures without losing statistical power as it is the case in standard subgroup analysis. This approach focuses on building a risk prediction signature for a specific stratum by down-weighting the observations (Simon, 2002) from the other strata using a range of weights. The not analyzed subgroup has to be indicated in stratnotinfocus, the observations of this subgroup, which is not in focus but should be retained in the analysis, are weighted down by different relative weights. Binder et al. (2012) propose such a weighting approach for high dimensional data but not for time-to-event endpoints. resample.CoxBoost performs a weighted regression approach for time-to-event data while using resampling over rep resampling data sets, specifically for evaluating the results. The different weights can be entered by mix.list which should be from a range between zero and one. For examining the variable selection stability for specific covariates as a function of the weights the beta coefficients are an output value of resample.CoxBoost, so that resampling inclusion frequencies (Sauerbrei et. al, 2011) can be calculated and visualized via the functions stabtrajec and weightfreqmap. For mandatory variables (Binder and Schumacher, 2008), which are only included for adjusting the model no penalty should be added, these can be indicated by the indices in unpen.index.

Value

resample.CoxBoost returns a list of length rep list elements with each list element being further a list of the following two objects:

CV.opt

number of optimal boosting steps obtained from cv.CoxBoost for each weight.
beta

beta coefficients obtained from CoxBoost as vector for each weight.

Author(s)

Written by Veronika Weyer [email protected] and Harald Binder [email protected].

References

Binder, H., Benner, A., Bullinger, L., Schumacher, M. (2013). Tailoring sparse multivariable regression techniques for prognostic single-nucleotide polymorphism signatures. Statistics in medicine 32(10), 1778-1791

Binder, H., Müller, T., Schwender, H., Golka, K., Steffens, M., G., H.J., Ickstadt, K., and Schumacher, M (2012). Cluster-Localized Sparse Logistic Regression for SNP Data. Statistical applications in genetics and molecular biology, 11(4), 1-31

Binder, H. and Schumacher, M. (2009). Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinformatics. 10:18.

Binder, H. and Schumacher, M. (2008). Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics. 9:14.

Sauerbrei, W., Boulesteix, A.-L., Binder, H. (2011). Stability investigations of multivariable regression models derived from low- and high-dimensional data. Journal of biopharmaceutical statistics 21(6), 1206-31

Simon, R. (2002). Bayesian subset analysis: application to studying treatment-by-gender interactions. Statistics in medicine 21(19), 2909-16

See Also

stabtrajec, weightfreqmap.

Examples

## Not run: 
# Generate survival data with five informative covariates for one subgroup
n <- 400; p <- 1000
set.seed(129)
group<-rbinom(n,1,0.5)
x <- matrix(rnorm(n*p,0,1),n,p)
beta.vec1  <- c(c(1,1,1,1,1),rep(0,p-5))
beta.vec0  <- c(c(0,0,0,0,0),rep(0,p-5))
linpred<-ifelse(group==1,x %*% beta.vec1,x %*% beta.vec0)
set.seed(1234)
real.time<- (-(log(runif(n)))/(1/20*exp(linpred)))
cens.time <- rexp(n,rate=1/20)
obs.status <- ifelse(real.time <= cens.time,1,0)
obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)


#   Fit a stratified weighted Cox proportional hazards model
# by \code{resample.CoxBoost}

RIF <- resample.CoxBoost(
  time=obs.time,status=obs.status,x=x,rep=100,
  maxstepno=200,multicore=FALSE,
  mix.list=c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99),
  stratum=group,stratnotinfocus=0,penalty=sum(obs.status)*(1/0.02-1),
  criterion="hscore",unpen.index=NULL)

#   RIF is a list with number of resampling data sets list objects, each list
# object has further two list objects, the beta coefficients for all
# weights and the selected number of boosting steps.
#   For each list object i \code{RIF[[i]]$beta} contains the beta
# coefficients with length mix.list*p for p covariates and with
# \code{RIF[[i]]$CV.opt}
#   For getting an insight in the RIF Distribution of different weights
# use the following code:

RIF1<-c()
for (i in 1: length(RIF)){RIF1<-c(RIF1,RIF[[i]][[1]])}
freqmat <-matrix(apply(matrix(unlist(RIF1), ncol=length(RIF))!=0,1,mean),
ncol=length(mix.list))

#  freqmat is a matrix with p rows and \code{length(mix.list)} columns
#  which contains resampling inclusion frequencies for the different
#  covariates and different weights.

## End(Not run)

Plots stability trajectories from resCoxBoost fits

Description

Plots the stability trajectories, i.e. the variable selection stability is plotted in form of resampling inclusion frequencies for different weights and a selection of different stably selected genes as obtained from a resCoxBoost object fitted by resample.CoxBoost.

Usage

stabtrajec(
  RIF,
  mix.list = c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99),
  plotmix = c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99),
  my.colors = grDevices::gray(seq(0.99, 0, len = 10)),
  yupperlim = 1,
  huge = 0.6,
  lowerRIFlimit = 0.6,
  legendval = 4.5
)

weightfreqmap(
  RIF,
  mix.list = c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99),
  plotmix = c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99),
  lowerRIFlimit = 0.5,
  method = "complete"
)

Arguments

RIF

list obtained from a resample.CoxBoost call.
mix.list

vector of weights which were also entered in the resample.CoxBoost call.
plotmix

vector of weights which should be plotted in the stability trajectory plot.
my.colors

vector with length(plotmix) of different colors for plotting the different weights, default are gray shades.
yupperlim

value for the upper y coordinate.
huge

size of the labels plottet on the x-axis.
lowerRIFlimit

cutoff RIF value: All covariates which have an resampling inclusion frequency (RIF) greater or equal this lowerRIFlimit value at any weight are presented in the stability trajectory plot.
legendval

space between the last plotted covariate and the legend on the right side of the plot.
method

passed to hclust for the the agglomeration method to be used

Details

The stabtrajec function produces a visualization tool (stability trajectory plot), which shows the RIFs as a function of weights for stably selected covariates selected via resample.CoxBoost. With this tool it can be shown that with weights between zero and one and so the additional information from the observations of the not analyzed subgroup in comparison to the standard subgroup analysis (weight near zero) can improve the variable selection stability. The number of plotted covariates in this plot depends on lowerRIFlimit. How many weights are plotted which are element of mix.list can be changed with plotmix.

Value

No value is returned, but the stability trajectory plot is generated.

Author(s)

Veronika Weyer [email protected] and Harald Binder [email protected]

Summary of a CoxBoost type

Description

Summary of a CoxBoost type

Usage

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

Arguments

object

a CoxBoost object
...

not used