InvariantConditionalQuantilePrediction {CondIndTests} R Documentation

## 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)

```

[Package CondIndTests version 0.1.5 Index]