CondIndTest {CondIndTests} R Documentation

## Wrapper function for conditional independence tests.

### Description

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

### Usage

```CondIndTest(Y, E, X, method = "KCI", alpha = 0.05,
parsMethod = list(), verbose = FALSE)
```

### Arguments

 `Y` An n-dimensional vector or a matrix or dataframe with n rows and p columns. `E` An n-dimensional vector or a matrix or dataframe with n rows and p columns. `X` An n-dimensional vector or a matrix or dataframe with n rows and p columns. `method` The conditional indepdence test to use, can be one of `"KCI"`, `"InvariantConditionalQuantilePrediction"`, `"InvariantEnvironmentPrediction"`, `"InvariantResidualDistributionTest"`, `"InvariantTargetPrediction"`, `"ResidualPredictionTest"`. `alpha` Significance level. Defaults to 0.05. `parsMethod` Named list to pass options to `method`. `verbose` If `TRUE`, intermediate output is provided. Defaults to `FALSE`.

### Value

A list with the p-value of the test (`pvalue`) and possibly additional entries, depending on the output of the chosen conditional independence test in `method`.

### References

Please cite C. Heinze-Deml, J. Peters and N. Meinshausen: "Invariant Causal Prediction for Nonlinear Models", arXiv:1706.08576 and the corresponding reference for the conditional independence test.

### Examples

```
# Example 1
set.seed(1)
n <- 100
Z <- rnorm(n)
X <- 4 + 2 * Z + rnorm(n)
Y <- 3 * X^2 + Z + rnorm(n)
test1 <- CondIndTest(X,Y,Z, method = "KCI")
cat("These data come from a distribution, for which X and Y are NOT
cond. ind. given Z.")
cat(paste("The p-value of the test is: ", test1\$pvalue))

# Example 2
set.seed(1)
Z <- rnorm(n)
X <- 4 + 2 * Z + rnorm(n)
Y <- 3 + Z + rnorm(n)
test2 <- CondIndTest(X,Y,Z, method = "KCI")
cat("The data come from a distribution, for which X and Y are cond.
ind. given Z.")
cat(paste("The p-value of the test is: ", test2\$pvalue))

```

[Package CondIndTests version 0.1.5 Index]