mamml.stat

The model-averaged mean maximized likelihood (MAMML) is defined as the (possibly weighted) arithmetic mean of the maximized likelihood ratios from a series of likelihood ratio tests comparing mutually exclusive alternative hypotheses with the same nested null hypothesis based on the exact same data.

 ~MAMML 

Compute the Model-Averaged Mean Maximized Likelihood

The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended.

Daniel Wilson

harmonicmeanp

Harmonic Mean p-Values and Model Averaging by Mean Maximum
Likelihood

Compute the Model-Averaged Mean Maximized Likelihood function

<dl> <dt>R</dt>
<dd>A numeric vector of one or more maximized likelihood ratios. Missing values (NAs) will cause a missing value to be returned.</dd> <dt>w</dt>
<dd>An optional numeric vector of weights that can be interpreted as prior model probabilities for each of the alternative hypotheses represented by the individual p-values. The sum of the weights cannot exceed one but may be less than one, which is interpreted as meaning that some p-values have been excluded.</dd></dl>

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Author

Compute the Model-Averaged Mean Maximized Likelihood — Compute the Model-Averaged Mean Maximized Likelihood

<dl>

 <dt>R</dt>
<dd>A numeric vector of one or more maximized likelihood ratios. Missing values (NAs) will cause a missing value to be returned.</dd>

 <dt>w</dt>
<dd>An optional numeric vector of weights that can be interpreted as prior model probabilities for each of the alternative hypotheses represented by the individual p-values. The sum of the weights cannot exceed one but may be less than one, which is interpreted as meaning that some p-values have been excluded.</dd>

</dl>

Compute the Model-Averaged Mean Maximized Likelihood: Compute the Model-Averaged Mean Maximized Likelihood

Description

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Examples