Description Usage Arguments Details Value See Also Examples

View source: R/stats_random_forest.R

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.

1 | ```
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()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
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)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.