# tests/testthat/test-bartMachine.R In SuperLearner: Super Learner Prediction

```#library(testthat)
#library(bartMachine)

if(all(sapply(c("testthat", "bartMachine"), requireNamespace))){
testthat::context("Learner: bartMachine")

# Create sample dataset for testing.
set.seed(1)
N <- 100
X <- matrix(rnorm(N*10), N, 10)
X <- as.data.frame(X)

# Binary outcome.
Y_bin <- rbinom(N, 1, plogis(.2*X[, 1] + .1*X[, 2] - .2*X[, 3] + .1*X[, 3]*X[, 4] - .2*abs(X[, 4])))
# table(Y_bin)

# Continuous outcome.
# Y_reg <- .2*X[, 1] + .1*X[, 2] - .2*X[, 3] + .1*X[, 3]*X[, 4] - .2*abs(X[, 4]) + rnorm(N)
# summary(Y_reg)

SL.library <- c("SL.mean", "SL.bartMachine")

# Test bartMachine - binary classification.
sl <- SuperLearner(Y = Y_bin, X = X, SL.library = SL.library,
cvControl = list(V = 2),
family = binomial())
sl

# Test bartMachine - regression.
#sl <- SuperLearner(Y = Y_reg, X = X, SL.library = SL.library,
#                   cvControl = list(V = 2),
#                   family = gaussian())
#sl

# TODO: test prediction.
}
```

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SuperLearner documentation built on May 10, 2021, 9:10 a.m.