Package 'BSGW'

Survival formula expressing the time/status variables as well as covariates used in regression on scale parameter. Currently, only right and left censoring is supported. Must include intercept term.

Data frame containing the covariates and response variable.

Formula expressing the covariates used in regression on shape parameter. No left-hand side is necessary since the response variable information is extracted from formula. Default value is formula. Must include intercept term.

Optional vector of case weights. *Not supported yet*

Subset of the observations to be used in the fit. *Not supported yet*

Missing-data filter function. *Not supported yet (only na.fail behavior works)*

Initialization behavior. Currently, three options are supported: 1) If init="survreg", an ordinary Weibull regression is performed and coefficients are used to initialize the bsgw MCMC run. 2) If init is a survreg object, e.g. from a previous Weibull regression fit, the object can be directly passed as parameter. 3) If init is any other value, or if survreg produces error or warning, we simply set all coefficients to zero.

If TRUE, a Bayesian ordinary Weibull model is estimated, in which any covariates in formulas are stripped away, and the inverse-logit transformation in the shape-parameter regression is replaced with a simple exponential transformation. If shrinkage parameters are kept at 0, the result is a Bayesian equivalent of an ordinary Weibull regression.

If scale>0, the value of the shape parameter is fixed, i.e. not estimated from data.

See bsgw.control for a description of the parameters inside the control list.

Controlling verbosity level.

If TRUE, each covariate vector is centered and scaled before model estimation. The scaling parameters are saved in return object, and used in subsequent calls to predict function. Users are strongly advised against turning this feature off, since the quality of Gibbs sampling MCMC is greatly enhanced by covariate centering and scaling.

Number of MCMC samples to draw.

Number of initial MCMC samples to discard before calculating summary statistics.

Threshold for standard deviation of a covariate (after possible centering/scaling). If below the threshold, the corresponding coefficient is removed from sampling, i.e. its value is clamped to zero.

Bayesian Lasso shrinkage parameter for scale-parameter coefficients.

Bayesian Lasso shrinkage parameter for shape-parameter coefficients.

Controlling how often to print progress report during MCMC run. For example, if nskip=10, progress will be reported after 10,20,30,... samples.

Lower bound on the shape parameter.

Upper bound on the shape parameter.

Upper bound on absolute value of coefficients of scale parameter (with the exception of the intercept).

Upper bound on absolute value of coefficients of shape parameter (with the exception of the intercept).

User-specified limit on total memory (in GB) available during prediction. Hazard, cumulative hazard, and survival prediction objects are all three-dimensional arrays which can quickly grow very large, depending on data length, number of MCMC samples collected, and number of time points along which prediction is made.

Object of class 'bsgw', usually the result of a call to the bsgw.

Arguments to be passed to/from other methods.

The matched call.

Same as input.

Same as input.

Same as input. *Not supported yet*

Same as input. *Not supported yet*

Same as input. *Not supported yet* (current behavior is na.fail)

Initial values for scale and shape coefficients used in MCMC sampling, either by performing an ordinary Weibull regression or by extracting estimated coefficients from a previously-performed such regression.

Same as input.

Value of scale parameter, estimated using ordinary Weibull regression by calling the survreg function in the survival package.

The "survreg" object returned from calling the same function for initialization of coefficients.

Same as input.

Same as input.

Model matrix used for regression on scale parameter, after potential centering and scaling. The corresponding vector of coefficients is called beta.

Model matrix used for regression on shape parameter, after potential centering and scaling. The corresponding vector of coefficients is called betas.

Survival response variable (time and status) used in the model.

The contrasts used for scale-parameter coefficients.

The contrasts used for shape-parameter coefficients.

A record of the levels of the factors used in fitting for scale parameter regression.

A record of the levels of the factors used in fitting for shape parameter regression.

The terms object used for scale parameter regression.

The terms object used for shape parameter regression.

Names of columns for X, also names of scale coefficients.

Names of columns for Xs, also names of shape coefficients.

Index of columns of X where scaling has been applied.

Index of columns of Xs where scaling has been applied.

Vector of centering parameters for columns of X indicated by apply.scale.X.

Vector of scaling parameters for columns of X indicated by apply.scale.X.

Vector of centering parameters for columns of Xs indicated by apply.scale.Xs.

Vector of scaling parameters for columns of Xs indicated by apply.scale.Xs.

Vector of indexes into X for which sampling occured. All columns of X whose standard deviation falls below sd.thresh are excluded from sampling and their corresponding coefficients are clamped to 0.

Vector of indexes into Xs for which sampling occured. All columns of Xs whose standard deviation falls below sd.thresh are excluded from sampling and their corresponding coefficients are clamped to 0.

List of median values, with elements including beta (coefficients of scale regression), betas (coefficients of shape regression), survreg.scale (value of surgreg-style scale parameter for all training set observations).

List of coefficient samples, with the following elements: 1) beta (scale parameter coefficients), 2) betas (shape parameter coefficients), 3) lp (vector of linear predictor for scale parameter, within-sample), 4) loglike (log-likelihood of model), 5) logpost (log-posterior of mode, i.e. log-likelihood plus the shrinkage term). The last two entities are used during within-sample prediction of response, i.e. during a subsequent call to predict. Each parameter has control$iter samples.

Kaplan-Meyer fit to training data. Used in plot.bsgw method.

Maximum time value in training set. Used in predict.bsgw for automatic selection of the tvec parameter.

Number of observations to create partitions for. It must typically be set to nrow(data).

Number of folds or partitions to generate.

Survival formula, used to extract the binary status field from the data. Right-hand side of the formula is ignored, so a formula of the form Surv(time,status)~1 is sufficient.

Data frame used in model training and prediction.

An integer vector of length nrow(data), defining fold/partition membership of each observation. For example, in 5-fold cross-validation for a data set of 200 observations, folds must be a 200-long vector with elements from the set {1,2,3,4,5}. Convenience functions bsgw.generate.folds and bsgw.generate.folds.eventbalanced can be used to generate the folds vector for a given survival data frame.

If TRUE, estimation objects from each cross-validation task is collected and returned for diagnostics purposes.

Verbosity of progress report.

List of control parameters, usually the output of bsgw.control.

Number of cores for parallel execution of cross-validation code.

Vector of shrinkage parameters to be tested for scale-parameter coefficients.

Vector of shrinkage parameters to be tested for shape-parameter coefficients.

A data frame that enumerates all combinations of lambda and lambdas to be tested. By default, it is constructed from forming all permutations of lambda.vec and lambdas.vec. If lambdas.vec=NULL, it will only try equal values of the two parameters in each combination.

If TRUE, and if the lambda and lambdas entries in lambda2 are identical, a plot of loglike as a function of either vector is produced.

Other arguments to be passed to bsgw.

Vector of log-likelihood values, one for each tested combination of lambda and lambdas.

The maximum log-likelihood value from the loglike.vec.

Data frame with columns lambda and lambdas. Each row of this data frame contains one combination of shrinkage parameters that are tested in the wrapper function.

If all=TRUE, a list of length nrow(lambda2) is returned, with each element being itself a list of nfold estimation objects associated with each call to the bsgw function. This object can be examined by the user for diagnostic purposes, e.g. by applying plot against each object.

A bsgw object, typically the output of bsgw function.

The P-value at which lower/upper bounds on coefficients are calculated and overlaid on trace plots and historgrams.

Number of samples discarded from the beginning of an MCMC chain, after which parameter quantiles are calculated.

Number of rows of subplots within each figure, applied to plot sets 2-4.

Number of columns of subplots within each figure, applied to plot sets 2-4.

Further arguments to be passed to/from other methods.

For predict.bsgw, an object of class "bsgw", usually the result of a call to bsgw; for summary.predict.bsgw, an object of class "predict.bsgw", usually the result of a call to predict.bsgw.

An optional data frame in which to look for variables with which to predict. If omiited, the fitted values (training set) are used.

An optional vector of time values, along which time-dependent entities (hazard, cumulative hazard, survival) will be predicted. If omitted, only the time-independent entities (currently only log-likelihood) will be calculated. If a single integer is provided for tvec, it is interpreted as number of time points, equally spaced from 0 to object$tmax: tvec <- seq(from=0.0, to=object$tmax, length.out=tvec).

Number of samples to discard from the beginning of each MCMC chain before calculating median value(s) for time-independent entities.

Number of cores to use for parallel prediction.

Further arguments to be passed to/from other methods.

Index of observations (rows of newdata or training data) for which to generate summary statistics. Default is the entire data.

Desired p-value, based on which lower/upper bounds will be calculated. Default is 0.05.

Whether population averages must be calculated or not. By default, population averages are only calculated when the entire data is included in prediction.

Whether population mean and other plots must be created or not.

Actual vector of time values (if any) used for prediction.

Same as input.

List of median values for predicted entities. Currently, only loglike and survreg.scale median is produced. See 'Details' for explanation.

List of MCMC samples for predicted entities. Elements include h (hazard function), H (cumulative hazard function), S (survival function), survreg.scale (inverse of shape parameter in rweibull), and loglike (model log-likelihood). All functions are evaluated over time values specified in tvec.

Kaplan-Meyer fit of the data used for prediction (if data contains response fields).

A list of lower-bound values for h, H, S, hr (hazard ratio of idx[2] to idx[1] observation), and S.diff (survival probability of idx[2] minus idx[1]). The last two are only included if length(idx)==2.

List of median values for same entities described in lower.

List of upper-bound values for same entities described in lower.

Lower-bound/median/upper-bound values for population average of survival probability.

Kaplan-Meyer fit associated with the prediction object (if available).

An object of class "bsgw", usually the result of a call to bsgw.

An object of class "summary.bsgw", usually the result of a call to summary.bsgw.

Desired p-value, based on which lower/upper bounds will be calculated. Default is 0.05.

Number of samples to discard from the beginning of each MCMC chain before calculating median and lower/upper bounds.

Further arguments to be passed to/from other methods.

The matched call.

Same as input.

Same as input.

A p x 4 matrix with columns for the estimated coefficient median, its lower and upper bounds given the user-supplied p-value, and the p-value for being smaller/larger than zero.

List of lower, median, and upper values of the survreg-style scale parameter (i.e. inverse of shape parameter in rweibull) for the training-set population.