softbart_regression {SoftBart} | R Documentation |
SoftBart Regression
Description
Fits a semiparametric regression model with the nonparametric function modeled using a SoftBart model.
Usage
softbart_regression(
formula,
data,
test_data,
num_tree = 20,
k = 2,
hypers = NULL,
opts = NULL,
verbose = TRUE
)
Arguments
formula |
A model formula with a numeric variable on the left-hand-side and predictors on the right-hand-side. |
data |
A data frame consisting of the training data. |
test_data |
A data frame consisting of the testing data. |
num_tree |
The number of trees in the ensemble to use. |
k |
Determines the standard deviation of the leaf node parameters, which is given by |
hypers |
A list of hyperparameters constructed from the |
opts |
A list of options for running the chain constructed from the |
verbose |
If |
Value
Returns a list with the following components:
-
sigma_mu
: samples of the standard deviation of the leaf node parameters. -
sigma
: samples of the error standard deviation. -
var_counts
: a matrix with a column for each predictor group containing the number of times each predictor is used in the ensemble at each iteration. -
mu_train
: samples of the nonparametric function evaluated on the training set. -
mu_test
: samples of the nonparametric function evaluated on the test set. -
mu_train_mean
: posterior mean ofmu_train
. -
mu_test_mean
: posterior mean ofmu_test
. -
formula
: the formula specified by the user. -
ecdfs
: empirical distribution functions, used by thepredict
function. -
opts
: the options used when running the chain. -
mu_Y, sd_Y
: used with the predict function to transform predictions. -
forest
: a forest object; see theMakeForest
documentation for more details.
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 + 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, 250, 100, 1)
df <- data.frame(X = sim_data$X, Y = sim_data$Y)
df_test <- data.frame(X = sim_data$X_test, Y = sim_data$Y_test)
## Fit the model
opts <- Opts(num_burn = num_burn, num_save = num_save)
fitted_reg <- softbart_regression(Y ~ ., df, df_test, opts = opts)
## Plot results
plot(colMeans(fitted_reg$mu_test), sim_data$mu_test)
abline(a = 0, b = 1)