View source: R/stats_random_forest.R

stat.random_forest | R Documentation |

Computes the difference statistic

*W_j = |Z_j| - |\tilde{Z}_j|*

where *Z_j* and *\tilde{Z}_j* are the random forest feature importances
of the jth variable and its knockoff, respectively.

stat.random_forest(X, X_k, y, ...)

`X` |
n-by-p matrix of original variables. |

`X_k` |
n-by-p matrix of knockoff variables. |

`y` |
vector of length n, containing the response variables. If a factor, classification is assumed, otherwise regression is assumed. |

`...` |
additional arguments specific to |

This function uses the `ranger`

package to compute variable
importance measures. The importance of a variable is measured as the total decrease
in node impurities from splitting on that variable, averaged over all trees.
For regression, the node impurity is measured by residual sum of squares.
For classification, it is measured by the Gini index.

For a complete list of the available additional arguments, see `ranger`

.

A vector of statistics *W* of length p.

Other statistics:
`stat.forward_selection()`

,
`stat.glmnet_coefdiff()`

,
`stat.glmnet_lambdadiff()`

,
`stat.lasso_coefdiff_bin()`

,
`stat.lasso_coefdiff()`

,
`stat.lasso_lambdadiff_bin()`

,
`stat.lasso_lambdadiff()`

,
`stat.sqrt_lasso()`

,
`stat.stability_selection()`

set.seed(2022) p=200; n=100; k=15 mu = rep(0,p); Sigma = diag(p) X = matrix(rnorm(n*p),n) nonzero = sample(p, k) beta = 3.5 * (1:p %in% nonzero) y = X %*% beta + rnorm(n) knockoffs = function(X) create.gaussian(X, mu, Sigma) # Basic usage with default arguments result = knockoff.filter(X, y, knockoffs=knockoffs, statistic=stat.random_forest) print(result$selected) # Advanced usage with custom arguments foo = stat.random_forest k_stat = function(X, X_k, y) foo(X, X_k, y, nodesize=5) result = knockoff.filter(X, y, knockoffs=knockoffs, statistic=k_stat) print(result$selected)

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