cov_importance_test {bartMachine} | R Documentation |

This function tests the null hypothesis *H_0*: These covariates of interest
do not affect the response under the assumptions of the BART
model.

cov_importance_test(bart_machine, covariates = NULL, num_permutation_samples = 100, plot = TRUE)

`bart_machine` |
An object of class “bart_machine”. |

`covariates` |
A vector of names of covariates of interest to be tested for having an effect on the response. A value of NULL indicates an omnibus test for all covariates having an effect on the response. If the name of a covariate is a factor, the entire factor will be permuted. We do not recommend entering the names of factor covariate dummies. |

`num_permutation_samples` |
The number of times to permute the covariates of interest and create a corresponding new BART model (see details). |

`plot` |
If |

To test the importance of a covariate or a set of covariates of interest on the response, this function generates
`num_permutations`

BART models with the covariate(s) of interest permuted (differently each time).
On each run, a measure of fit is recorded. For regression, the metric is Pseudo-Rsq; for classification, it is
total misclassification error.

A
p-value can then be generated as follows. For regression, the p-value is the number of
permutation-sampled Pseudo-Rsq's greater than the observed Pseudo-Rsq divided by
`num_permutations + 1`

. For classification, the p-value is the number of permutation-sampled
total misclassification errors less than the observed total misclassification error divided by `num_permutations + 1`

.

`permutation_samples_of_error` |
A vector which records the error metric of the BART models with the covariates permuted (see details). |

`observed_error_estimate` |
For regression, this is the Pseudo-Rsq on the original training data set. For classification, this is the observed total misclassification error on the original training data set. |

`pval` |
The approximate p-value for this test (see details). |

This function is parallelized by the number of cores set in `set_bart_machine_num_cores`

.

Adam Kapelner and Justin Bleich

Adam Kapelner, Justin Bleich (2016). bartMachine: Machine Learning with Bayesian Additive Regression Trees. Journal of Statistical Software, 70(4), 1-40. doi:10.18637/jss.v070.i04

## Not run: ##regression example ##generate Friedman data set.seed(11) n = 200 p = 5 X = data.frame(matrix(runif(n * p), ncol = p)) y = 10 * sin(pi* X[ ,1] * X[,2]) +20 * (X[,3] -.5)^2 + 10 * X[ ,4] + 5 * X[,5] + rnorm(n) ##build BART regression model bart_machine = bartMachine(X, y) ##now test if X[, 1] affects Y nonparametrically under the BART model assumptions cov_importance_test(bart_machine, covariates = c(1)) ## note the plot and the printed p-value ## End(Not run)

[Package *bartMachine* version 1.2.6 Index]