Criterion {Mediana}

Criterion object

Description

This function creates an object of class Criterion which can be added to an object of class EvaluationModel.

Usage

Criterion(id, method, tests = NULL, statistics = NULL, par = NULL, labels)

Arguments

id

defines the ID of the Criterion object.

method

defines the method used by the Criterion object.

tests

defines the test(s) used by the Criterion object.

statistics

defines the statistic(s) used by the Criterion object.

par

defines the parameter(s) of the method argument of the Criterion object .

labels

defines the label(s) of the results.

Details

Objects of class Criterion are used in objects of class EvaluationModel to specify the criteria that will be applied to the Clinical Scenario. Several objects of class Criterion can be added to an object of class EvaluationModel.

Mandatory arguments are id, method, labels and tests and/or statistics.

method argument defines the criterion's method. Several methods are already implemented in the Mediana package (listed below, along with the required parameters to define in the par parameter):

• MarginalPower: generate the marginal power of all tests defined in the test argument. Required parameter: alpha.

• WeightedPower: generate the weighted power of all tests defined in the test argument. Required parameters: alpha and weight.

• DisjunctivePower: generate the disjunctive power (probability to reject at least one hypothesis defined in the test argument). Required parameter: alpha.

• ConjunctivePower: generate the conjunctive power (probability to reject all hypotheses defined in the test argument). Required parameter: alpha.

• ExpectedRejPower: generate the expected number of rejected hypotheses. Required parameter: alpha.

References

http://gpaux.github.io/Mediana/

See Also

See Also AnalysisModel.

Examples

## Add a Criterion to an EvaluationModel object
evaluation.model = EvaluationModel() +
                   Criterion(id = "Marginal power",
                   method = "MarginalPower",
                   tests = tests("Placebo vs treatment"),
                   labels = c("Placebo vs treatment"),
                   par = parameters(alpha = 0.025))