# testMultipleControl: Multiple Comparison Statistical Test (Friedman + Control Holm... In exreport: Fast, Reliable and Elegant Reproducible Research

## Description

This function perfoms a multiple comparison statistical test for the given experiment. First of all it performs a Friedman Test over all methods. In the case this test is rejected, meaning that significant differences are present among the methods a post-hoc test is then executed. For that, a comparison using the best method as a control is performed for each other method, finally a Holm familywise error correction is applied to the resulting p-values.

## Usage

 `1` ```testMultipleControl(e, output, rankOrder = "max", alpha = 0.05) ```

## Arguments

 `e` Input experiment `output` The output for which the tet will be performed. `rankOrder` The optimization strategy, can be either maximizing "max" or minimizing "min" the target output variable. `alpha` The significance level used for the whole testing procedure.

## Value

an testMultipleControl object

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# First we create an experiment from the wekaExperiment problem and prepare # it to apply the test: experiment <- expCreate(wekaExperiment, name="test", parameter="fold") experiment <- expReduce(experiment, "fold", mean) experiment <- expSubset(experiment, list(featureSelection = "yes")) experiment <- expInstantiate(experiment, removeUnary=TRUE) # Then we perform a testMultiplePairwise test procedure test <- testMultipleControl(experiment, "accuracy", "max") summary(test) ```

### Example output

```---------------------------------------------------------------------
Friedman test, objetive maximize output variable accuracy. Obtained p-value: 3.3072e-04
Chi squared with 3 degrees of freedom statistic: 18.6000
Test rejected: p-value: 3.3072e-04 < 0.0500
---------------------------------------------------------------------
Control post hoc test for output accuracy