Bayesian Additive Regression Trees with Random Effects
Fits a varying intercept/random effect BART model.
rbart_vi(
formula, data, test, subset, weights, offset, offset.test = offset,
group.by, group.by.test, prior = cauchy,
sigest = NA_real_, sigdf = 3.0, sigquant = 0.90,
k = 2.0,
power = 2.0, base = 0.95,
n.trees = 75L,
n.samples = 1500L, n.burn = 1500L,
n.chains = 4L, n.threads = min(guessNumCores(), n.chains), combineChains = FALSE,
n.cuts = 100L, useQuantiles = FALSE,
n.thin = 5L, keepTrainingFits = TRUE,
printEvery = 100L, printCutoffs = 0L,
verbose = TRUE,
keepTrees = TRUE, keepCall = TRUE, ...)
## S3 method for class 'rbart'
plot(x, plquants = c(0.05, 0.95), cols = c('blue', 'black'), ...)
## S3 method for class 'rbart'
fitted(object,
type = c("ev", "ppd", "bart", "ranef"),
sample = c("train", "test"),
combineChains = TRUE,
...)
## S3 method for class 'rbart'
extract(object,
type = c("ev", "ppd", "bart", "ranef"),
sample = c("train", "test"),
combineChains = TRUE,
...)
## S3 method for class 'rbart'
predict(object, newdata, group.by, offset,
type = c("ev", "ppd", "bart", "ranef"),
combineChains = TRUE,
...)
## S3 method for class 'rbart'
residuals(object, ...)group.by |
Grouping factor. Can be an integer vector/factor, or a reference to such in |
group.by.test |
Grouping factor for test data, of the same type as |
prior |
A function or symbolic reference to built-in priors. Determines the prior over the standard deviation of
the random effects. Supplied functions take two arguments, |
n.thin |
The number of tree jumps taken for every stored sample, but also the number of samples from the posterior of the standard deviation of the random effects before one is kept. |
formula, data, test, subset, weights, offset, offset.test, sigest, sigdf, sigquant,
k, power, base, n.trees, n.samples, n.burn, n.chains, n.threads, combineChains,
n.cuts, useQuantiles, keepTrainingFits, printEvery, printCutoffs, verbose,
keepTrees, keepCall, ... |
Same as in |
object |
A fitted |
newdata |
Same as |
type |
One of |
sample |
One of |
x, plquants, cols |
Same as in |
Fits a BART model with additive random intercepts, one for each factor level of group.by. For continuous responses:
y_i ~ N(f(x_i) + α_{g[i]}, σ^2)
α_j ~ N(0, τ^2).
For binary outcomes the response model is changed to P(Y_i = 1) = Φ(f(x_i) + α_{g[i]}). i indexes observations, g[i] is the group index of observation i, f(x) and σ_y come from a BART model, and α_j are the independent and identically distributed random intercepts.
Predicting random effects for groups not in the training sample is supported by sampling
from their posterior predictive distribution, that is a draw is taken from
p(α \mid y) = \int p(α \mid τ)p(τ \mid y)dα.
For out-of-sample groups in the test data, these random effect draws can be kept
with the saved object. For those supplied to predict, they cannot and may
change for subsequent calls.
See the generics section of bart.
An object of class rbart. Contains all of the same elements of an object of class bart,
as well as the elements
ranef |
Samples from the posterior of the random effects. A array/matrix of posterior samples.
The (k, l, j) value is the lth draw of the posterior of the random effect for group
j (i.e. α*_j) corresponding to chain k. When |
ranef.mean |
Posterior mean of random effects, derived by taking mean across group index of samples. |
tau |
Matrix of posterior samples of |
|
Burn-in draws of |
Vincent Dorie: vdorie@gmail.com
f <- function(x) {
10 * sin(pi * x[,1] * x[,2]) + 20 * (x[,3] - 0.5)^2 +
10 * x[,4] + 5 * x[,5]
}
set.seed(99)
sigma <- 1.0
n <- 100
x <- matrix(runif(n * 10), n, 10)
Ey <- f(x)
y <- rnorm(n, Ey, sigma)
n.g <- 10
g <- sample(n.g, length(y), replace = TRUE)
sigma.b <- 1.5
b <- rnorm(n.g, 0, sigma.b)
y <- y + b[g]
df <- as.data.frame(x)
colnames(df) <- paste0("x_", seq_len(ncol(x)))
df$y <- y
df$g <- g
## low numbers to reduce run time
rbartFit <- rbart_vi(y ~ . - g, df, group.by = g,
n.samples = 40L, n.burn = 10L, n.thin = 2L, n.chains = 1L,
n.trees = 25L, n.threads = 1L)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.