Description Usage Arguments Value Examples

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.

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

`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. |

an testMultipleControl object

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)
``` |

```
---------------------------------------------------------------------
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
Adjust method: Holm
Control method: RandomForest
p-values:
J48 0.2579
NaiveBayes 0.0324
OneR 0.0001
---------------------------------------------------------------------
```

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