InvariantConditionalQuantilePrediction {CondIndTests}

Invariant conditional quantile prediction.

Description

Tests the null hypothesis that Y and E are independent given X.

Usage

InvariantConditionalQuantilePrediction(Y, E, X, alpha = 0.05,
  verbose = FALSE, test = fishersTestExceedance,
  mtry = sqrt(NCOL(X)), ntree = 100, nodesize = 5, maxnodes = NULL,
  quantiles = c(0.1, 0.5, 0.9), returnModel = FALSE)

Arguments

Y

An n-dimensional vector.

E

An n-dimensional vector. If test = fishersTestExceedance, E needs to be a factor.

X

A matrix or dataframe with n rows and p columns.

alpha

Significance level. Defaults to 0.05.

verbose

If TRUE, intermediate output is provided. Defaults to FALSE.

test

Unconditional independence test that tests whether exceedence is independent of E. Defaults to fishersTestExceedance.

mtry

Random forest parameter: Number of variables randomly sampled as candidates at each split. Defaults to sqrt(NCOL(X)).

ntree

Random forest parameter: Number of trees to grow. Defaults to 100.

nodesize

Random forest parameter: Minimum size of terminal nodes. Defaults to 5.

maxnodes

Random forest parameter: Maximum number of terminal nodes trees in the forest can have. Defaults to NULL.

quantiles

Quantiles for which to test independence between exceedence and E. Defaults to c(0.1, 0.5, 0.9).

returnModel

If TRUE, the fitted quantile regression forest model will be returned. Defaults to FALSE.

Value

A list with the following entries:

• pvalue The p-value for the null hypothesis that Y and E are independent given X.

• model The fitted quantile regression forest model if returnModel = TRUE.

Examples

# Example 1
n <- 1000
E <- rbinom(n, size = 1, prob = 0.2)
X <- 4 + 2 * E + rnorm(n)
Y <- 3 * (X)^2 + rnorm(n)
InvariantConditionalQuantilePrediction(Y, as.factor(E), X)

# Example 2
E <- rbinom(n, size = 1, prob = 0.2)
X <- 4 + 2 * E + rnorm(n)
Y <- 3 * E + rnorm(n)
InvariantConditionalQuantilePrediction(Y, as.factor(E), X)