predict.aftree: Estimating the survival and the hazard for AFT BART models.

In nftbart: Nonparametric Failure Time Bayesian Additive Regression Trees

Estimating the survival and the hazard for AFT BART models.

Description

The function predict.aftree() is provided for performing posterior inference via test data set estimates stored in a aftree object returned from AFTree() in a similar fashion as that of predict.nft. N.B. the x.test matrix must be provided on the AFTree() function call. Here we are only calculating the survival function by default, and, if requested, the hazard as well.

Usage

## S3 method for class 'aftree'
predict(
            ## data
            object,
            ## predictions
            events=NULL,
            FPD=FALSE,
            probs=c(0.025, 0.975),
            take.logs=TRUE,
            seed=NULL,
            ## default settings
            ndpost=nrow(object$mix.prop),
            nclust=ncol(object$mix.prop),
            ## etc.
            ...)

Arguments

object	Object of type nft from a previous call to nft().
events	You must specify a grid of time-points; however, they can be a matrix with rows for each subject.
FPD	Whether to yield the usual predictions or marginal predictions calculated by the partial dependence function.

probs	A vector of length two containing the lower and upper quantiles to be calculated for the predictions.
take.logs	Whether or not to take logarithms.
seed	If provided, then this value is used to generate random natural logarithms of event times from the predictive distribution.
ndpost	The number of MCMC samples generated.
nclust	The number of DPM clusters generated.
...	The et cetera objects passed to the predict method. Currently, it has no functionality.

Details

Returns a list with the following entries. If hazard=TRUE is specified, then a similar set of entries for the hazard are produced.

Value

surv.fpd	Survival function posterior draws on a grid of time-points by the partial dependence function when requested.
surv.fpd.mean	Survival function estimates on a grid of time-points by the partial dependence function when requested.
surv.fpd.lower	Survival function lower quantiles on a grid of time-points by the partial dependence function when requested.
surv.fpd.upper	Survival function upper quantiles on a grid of time-points by the partial dependence function when requested.

Author(s)

Rodney Sparapani: [e-mail]

See Also

predict.nft

nftbart documentation built on May 1, 2023, 1:08 a.m.