Convenience function to create a control object for use with a dbarts sampler.
dbartsControl(
verbose = FALSE, keepTrainingFits = TRUE, useQuantiles = FALSE,
keepTrees = FALSE, n.samples = NA_integer_,
n.cuts = 100L, n.burn = 200L, n.trees = 75L, n.chains = 4L,
n.threads = dbarts::guessNumCores(), n.thin = 1L, printEvery = 100L,
printCutoffs = 0L,
rngKind = "default", rngNormalKind = "default", rngSeed = NA_integer_,
updateState = TRUE)An object of class dbartControl.
Logical controlling sampler output to console.
Logical controlling whether or not training fits are returned when the sampler runs. These are always computed as part of the fitting procedure, so disabling will not substantially impact running time.
Logical to determine if the empirical quantiles of a columns of predictors should be used to determine the tree decision rules. If FALSE, the rules are spaced uniformly throughout the range of covariate values.
A logical that determines whether or not trees are cached as they are sampled. In all cases, the current state of the sampler is stored as a single set of n.trees. When keepTrees is TRUE, a set of n.trees * n.samples trees are set aside and populated as the sampler runs. If the sampler is stopped and restarted, samples proceed from the previously stored tree, looping over if necessary.
A non-negative integer giving the default number of samples to return each time the sampler is run. Generally specified by dbarts instead, and can be overridden on a per-use basis whenever the sampler is run.
A positive integer or integer vector giving the number of decision rules to be used for each given predictor. If of length less than the number of predictors, earlier values are recycled. If for any predictor more values are specified than are coherent, fewer may be used. See details for more information.
A non-negative integer determining how many samples, if any, are thrown away at the beginning of a run of the sampler.
A positive integer giving the number of trees used in the sum-of-trees formulation.
A positive integer detailing the number of independent chains for the sampler to use.
A positive integer controlling how many threads will be used for various internal calculations, as well as the number of chains. Internal calculations are highly optimized so that single-threaded performance tends to be superior unless the number of observations is very large (>10k), so that it is often not necessary to have the number of threads exceed the number of chains.
A positive integer determining how many iterations the MCMC chain should jump on the decision trees alone before recording a sample. Serves to “thin” the samples against serial correlation. n.samples are returned regardless of the value of n.thin.
If verbose is TRUE, every printEvery potential samples (after thinning) will issue a verbal statement. Must be a positive integer.
A non-negative integer specifying how many of the decision rules for a variable are printed in verbose mode.
Random number generator kind, as used in set.seed. For type "default", the built-in generator will be used if possible. Otherwise, will attempt to match the built-in generator's type. Success depends on the number of threads.
Random number generator normal kind, as used in set.seed. For type "default", the built-in generator will be used if possible. Otherwise, will attempt to match the built-in generator's type. Success depends on the number of threads and the rngKind.
Random number generator seed, as used in set.seed. If the sampler is running single-threaded or has one chain, the behavior will be as any other sequential algorithm. If the sampler is multithreaded, the seed will be used to create an additional pRNG object, which in turn will be used sequentially seed the thread-specific pRNGs. If equal to NA, the clock will be used to seed pRNGs when applicable.
Logical setting the default behavior for many sampler methods with regards to the immediate updating of the cached state of the object. A current, cached state is only useful when saving/loading the sampler.
dbarts