dbarts_wrapper: Wrapper for fitting Bayesian additive regression trees (BART)...
In benkeser/predtmle: Small sample estimators of cross-validated prediction metrics

Description Usage Arguments Details Value Examples

Compatible learner wrappers for this package should have a specific format. Namely they should take as input a list called train that contains named objects $Y and $X, that contain, respectively, the outcomes and predictors in a particular training fold. Other options may be passed in to the function as well. The function must output a list with the following named objects: test_pred = predictions of test$Y based on the learner fit using train$X; train_pred = prediction of train$Y based on the learner fit using train$X; model = the fitted model (only necessary if you desire to look at this model later, not used for internal computations); train_y = a copy of train$Y; test_y = a copy of test$Y.

dbarts_wrapper(test, train, sigest = NA, sigdf = 3, sigquant = 0.9,
  k = 2, power = 2, base = 0.95, binaryOffset = 0, ntree = 200,
  ndpost = 1000, nskip = 100, printevery = 100, keepevery = 1,
  keeptrainfits = TRUE, usequants = FALSE, numcut = 100,
  printcutoffs = 0, nthread = 1, keepcall = TRUE, verbose = FALSE)

`test`	A list with named objects `Y` and `X` (see description).
`train`	A list with named objects `Y` and `X` (see description).
`sigest`	See dbarts
`sigdf`	See dbarts
`sigquant`	See dbarts
`k`	See dbarts
`power`	See dbarts
`base`	See dbarts
`binaryOffset`	See dbarts
`ntree`	See dbarts
`ndpost`	See dbarts
`nskip`	See dbarts
`printevery`	See dbarts
`keepevery`	See dbarts
`keeptrainfits`	See dbarts
`usequants`	See dbarts
`numcut`	See dbarts
`printcutoffs`	See dbarts
`nthread`	See dbarts
`keepcall`	See dbarts
`verbose`	See dbarts

This particular wrapper implements Bayesian additive regression trees using dbarts. We refer readers to the original package's documentation for more details.

A list with named objects (see description).

# simulate data
Q0 <- function(x){ plogis(x) }
# make list of training data
train_X <- data.frame(x1 = runif(50))
train_Y <- rbinom(50, 1, Q0(train_X$x1))
train <- list(Y = train_Y, X = train_X)
# make list of test data
test_X <- data.frame(x1 = runif(50))
test_Y <- rbinom(50, 1, Q0(train_X$x1))
test <- list(Y = test_Y, X = test_X)
# fit dbarts
dbarts_wrap <- dbarts_wrapper(train = train, test = test)