tuneEcross: Utility function for tuning expected number of crossings for tsbart().

This function provides tunes the expected number of crossings. See vignette for detail.

tuneEcross(ecross_candidates = seq(0.25, 5, by = 0.25), y, tgt,
          x, nburn = 1000, nsim = 1000, ntree = 200, lambda = NULL,
          sigq = 0.9, sighat = NULL, nu = 3, base_tree = 0.95,
          power_tree = 2, probit = FALSE, yobs = NULL,
          monotone = "no", binsize = NULL)

Arguments

ecross_candidates	A vector of candidate values for expected number of crossings. Required, defaults to seq(.25,5,by=.25).
y	Length n vector with continuous response values. In probit case, should contain initializations for latent variables.
tgt	Length n targeted covariate over which to smooth.
x	A (n x p) data frame or matrix containing the covariates which are not to be smoothed over.
nburn	Number of burn-in MCMC iterations.
nsim	Number of MCMC iterations to save after burn-in.
ntree	Number of trees. Defaults to 200.
lambda	Scale parameter in the chisq prior sigma^2. Defaults to NULL, ie being estimated from sigq and sighat. Not appicable for binary case.
sigq	Calibration quantile in the chisq prior on sigma^2. Defaults to .9. Not applicable for binary case.
sighat	Calibration estimate for chisq prior on sigma^2. Defaults to being estimated from data using linear model. Not applicable for binary case.
nu	Degrees of freedom in the chisq prior on sigma^2. Defaults to 3. Not applicable for binary case.
base_tree	Base for tree prior. Defaults to 0.95.
power_tree	Power for tree prior. Defaults to 2.0.
probit	F indicates continuous response; T indicates probit response. Default is F. If T, then yobs must be populated.
yobs	Length n vector of binary responses; only populated for probit=T case.
monotone	For Projective Smooth BART. Specifies type of monotonicity constraint on f(x,t) estimates. If "no", no monotonicity constraint. If "incr" or "decr", f(x,t) is monotone increasing or decreasing. Default is "no".
binsize	Specifies a value $c$ where (y_i mod c == 0) indicates that y_i may have been rounded to this bin size. Defaults to NULL.

Value

A list object, containing the following items:

ecross_opt

A double; the optimal expected crossings value from the user input grid.

waic_plot

A ggplot object containing diagnostics for tuning expected crossings.

waic_grid

A data frame containing the expected crossing candidates and corresponding WAIC values.

Examples

if (FALSE) {
## Evaluate optimal number of crossings.
ecrossTune = tuneEcross(ecross_candidates = seq(.25,5,by=.25),
                      y, tgt, x, nburn, nsim, ntree=200,
                      lambda=NULL, sigq=.9, sighat=NULL, nu=3,
                      base_tree=.95, power_tree=2,
                      probit=FALSE, yobs=NULL, monotone="no",
                      binsize=NULL)

## View expected number of crossings.
ecrossTune[["ecross_opt"]]

}