Description Usage Arguments Details Value Author(s) See Also Examples
Fits a varying intercept/random effect BART model.
For numeric response y_i = f(x_i) + α_{j[i]} + ε_i, where ε_i ~ N(0, σ_y^2) and α_j ~ N(0, σ_α^2).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  rbart_vi(
formula, data, test, subset, weights, offset, offset.test = offset,
group.by, 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'
predict(object, test, group.by, offset.test, combineChains, ...)

group.by 
Grouping factor. Can be an integer vector/factor, or a reference to such in 
prior 
A function or symbolic reference to builtin 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 
Same as in 
x, plquants, cols 
Same as in 
Fits a BART model with additive random intercepts, one for each factor level of group.by
. That is
y_i = b_g[i] + f(x_i) + ε,
b_j ~ N(0, τ^2).
where i indices observations, g[i] is the group index of observation i, f(x) and ε come from a BART model, and b_j are the independent and identically distributed random intercepts.
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 (i, j, k) value is the jth draw of the posterior of the random effect for group
k (i.e. b*__k) corresponding to chain i. When 
ranef.mean 
Posterior mean of random effects, derived by taking mean across group index of samples. 
tau 
Matrix of posterior samples of 

Burnin draws of 
Vincent Dorie: [email protected]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  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)

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