# R/stats_random_forest.R In knockoff: The Knockoff Filter for Controlled Variable Selection

#### Documented in stat.random_forest

#' Importance statistics based on random forests
#'
#' Computes the difference statistic
#'   \deqn{W_j = |Z_j| - |\tilde{Z}_j|}
#' where \eqn{Z_j} and \eqn{\tilde{Z}_j} are the random forest feature importances
#' of the jth variable and its knockoff, respectively.
#'
#' @param X n-by-p matrix of original variables.
#' @param X_k n-by-p matrix of knockoff variables.
#' @param y vector of length n, containing the response variables. If a factor, classification is assumed,
#' otherwise regression is assumed.
#' @param ... additional arguments specific to \code{ranger} (see Details).
#' @return A vector of statistics \eqn{W} of length p.
#'
#' @details This function uses the \code{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 \code{\link[ranger]{ranger}}.
#'
#' @family statistics
#'
#' @examples
#' 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)
#'
#' @rdname stat.random_forest
#' @export
stat.random_forest <- function(X, X_k, y, ...) {
if (!requireNamespace('ranger', quietly=T))
stop('ranger is not installed', call.=F)

# Randomly swap columns of X and Xk
swap = rbinom(ncol(X),1,0.5)
swap.M = matrix(swap,nrow=nrow(X),ncol=length(swap),byrow=TRUE)
X.swap  = X * (1-swap.M) + X_k * swap.M
Xk.swap = X * swap.M + X_k * (1-swap.M)

# Compute statistics
Z = random_forest_importance(cbind(X.swap, Xk.swap), y)
p = ncol(X)
orig = 1:p
W = abs(Z[orig]) - abs(Z[orig+p])

# Correct for swapping of columns of X and Xk
W = W * (1-2*swap)
}

#' @keywords internal
random_forest_importance <- function(X, y, ...) {
df = data.frame(y=y, X=X)
rfFit = ranger::ranger(y~., data=df, importance="impurity", write.forest=F, ...)
as.vector(rfFit\$variable.importance)
}


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knockoff documentation built on July 2, 2020, 12:02 a.m.