mc.surv.bart.gse: Global SE variable selection for nonparametric survival...

In timebart: timebart: Survival Analysis with BART

Description

Here we implement the global SE method for variable selection in nonparametric survival analysis with BART. Unfortunately, the method is very computationally intensive so we present some trade-offs below.

Usage

mc.surv.bart.gse( x.train, times, delta,
                 P=50L, R=5L, ntree=20L, C=1, alpha=0.05,
                 k=2.0, power=2.0, base=.95,
                 binaryOffset=NULL,
                 ndpost=2000L, nskip=50L,
                 printevery=100L, keepevery=1L, keeptrainfits=FALSE,
                 usequants=FALSE, numcut=100L, printcutoffs=0L,
                 verbose=TRUE,
                 seed=99L, mc.cores=2L, nice=19L 
                 )

Arguments

x.train	Explanatory variables for training (in sample) data. Must be a matrix with (as usual) rows corresponding to observations and columns to variables. surv.bart will generate draws of f(t, x) for each x which is a row of x.train.
times	The time of event or right-censoring.
delta	The event indicator: 1 is an event while 0 is censored.
P	The number of permutations: typically 50 or 100.
R	The number of replicates: typically 5 or 10.
ntree	The number of trees. In variable selection, the number of trees is smaller than what might be used for the best fit.
C	The starting value for the multiple of SE. You should not need to change this except in rare circumstances.
alpha	The global SE method relies on simultaneous 1-alpha coverage across the permutations for all predictor variables.
k	k is the number of prior standard deviations f(t, x) is away from +/-3. The bigger k is, the more conservative the fitting will be.
power	Power parameter for tree prior.
base	Base parameter for tree prior.
binaryOffset	If binaryOffset=NULL, then this is the same as setting binaryOffset=0.
ndpost	The number of posterior draws after burn in. In the global SE method, generally, the method is repeated several times to establish the variable count probabilities. However, we take the alternative approach of simply running the MCMC chain longer which should result in the same stabilization of the estimates. Therefore, the number of posterior draws in variable selection should be set to a larger value than would be typically anticipated for fitting.
nskip	Number of MCMC iterations to be treated as burn in.
printevery	As the MCMC runs, a message is printed every printevery draws.
keepevery	Every keepevery draw is kept.
keeptrainfits	If TRUE the draws of f(t, x) for x = rows of x.train are generated.
usequants	Decision rules in the tree are of the form x <= c vs. x > c for each variable corresponding to a column of x.train. usequants determines how the set of possible c is determined. If usequants is true, then the c are a subset of the values (xs[i]+xs[i+1])/2 where xs is unique sorted values obtained from the corresponding column of x.train. If usequants is false, the cutoffs are equally spaced across the range of values taken on by the corresponding column of x.train.
numcut	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the i^th element gives the number of c used for the i^th variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
printcutoffs	The number of cutoff rules c to printed to screen before the MCMC is run. Give a single integer, the same value will be used for all variables. If 0, nothing is printed.
verbose	Logical, if FALSE supress printing.
seed	seed required for reproducible MCMC.
mc.cores	Number of cores to employ in parallel.
nice	Set the job priority. The default priority is 19: priorities go from 0 (highest) to 19 (lowest).

Value

mc.surv.bart.gse returns a list.

Author(s)

Rodney Sparapani: [e-mail]

References

Bleich, J., Kapelner, A., George, E.I., and Jensen, S.T. (2014). Variable selection for BART: an application to gene regulation. The Annals of Applied Statistics, 8:1750-81.

See Also

mc.surv.bart

Examples

## Not run: 
require(timebart)

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timebart documentation built on May 2, 2019, 4:43 p.m.