Continue sampling a model on new data.
Value
List of sampling outputs and a wrapper around the sampled forests (which can be used for in-memory prediction on new data, or serialized to JSON on disk).
Examples
n <- 100
p <- 5
X <- matrix(runif(n*p), ncol = p)
f_XW <- (
((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5) +
((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5) +
((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5) +
((0.75 <= X[,1]) & (1 > X[,1])) * (7.5)
)
noise_sd <- 1
y <- f_XW + rnorm(n, 0, noise_sd)
test_set_pct <- 0.2
n_test <- round(test_set_pct*n)
n_train <- n - n_test
test_inds <- sort(sample(1:n, n_test, replace = FALSE))
train_inds <- (1:n)[!((1:n) %in% test_inds)]
X_test <- X[test_inds,]
X_train <- X[train_inds,]
y_test <- y[test_inds]
y_train <- y[train_inds]
bart_model <- bart(X_train = X_train, y_train = y_train,
num_gfr = 10, num_burnin = 0, num_mcmc = 10)
continueSampling(bart_model, X_train = X_train, y_train = y_train,
num_gfr = 10, num_burnin = 0, num_mcmc = 10)
#> stochtree::bart() run with mean forest, global error variance model, and mean forest leaf scale model
#> Continuous outcome was modeled as Gaussian with a constant leaf prior for the mean forest
#> Outcome was standardized
#> The sampler was run for 20 GFR iterations, with 1 chain of 0 burn-in iterations and 20 MCMC iterations, retaining every iteration (i.e. no thinning)