posterior_probs {SoftBart}

BART Posterior Inclusion Probabilities

Description

Computes the posterior inclusion probabilities (PIPs) for the fitted SoftBART model, as well as variable importances and the median probability model (MPM).

Usage

posterior_probs(fit)

Arguments

fit

An object of class softbart, softbart_regression, or softbart_probit.

Value

A list containing the following:

• varimp: a vector containing the average number of times a predictor was used in a splitting rule.

• post_probs: the posterior inclusion probabilities for each predictor.

• median_probability_model: a vector containing the indicies of the variables included in at least 50 percent of the samples.

Examples

## NOTE: SET NUMBER OF BURN IN AND SAMPLE ITERATIONS HIGHER IN PRACTICE

num_burn <- 10 ## Should be ~ 5000
num_save <- 10 ## Should be ~ 5000

set.seed(1234)
f_fried <- function(x) 10 * sin(pi * x[,1] * x[,2]) + 20 * (x[,3] - 0.5)^2 + 
  10 * x[,4] + 5 * x[,5]

gen_data <- function(n_train, n_test, P, sigma) {
  X <- matrix(runif(n_train * P), nrow = n_train)
  mu <- f_fried(X)
  X_test <- matrix(runif(n_test * P), nrow = n_test)
  mu_test <- f_fried(X_test)
  Y <- mu + sigma * rnorm(n_train)
  Y_test <- mu_test + sigma * rnorm(n_test)
  
  return(list(X = X, Y = Y, mu = mu, X_test = X_test, Y_test = Y_test, mu_test = mu_test))
}

## Simiulate dataset
sim_data <- gen_data(250, 100, 1000, 1)

## Fit the model
fit <- softbart(X = sim_data$X, Y = sim_data$Y, X_test = sim_data$X_test, 
                hypers = Hypers(sim_data$X, sim_data$Y, num_tree = 50, temperature = 1),
                opts = Opts(num_burn = num_burn, num_save = num_save, update_tau = TRUE))
                
## Variable selection

post_probs <- posterior_probs(fit)
plot(post_probs$post_probs)
print(post_probs$median_probability_model)