bristlecone
Akaike Module
Functions for conducting Akaike Information Criterion (AIC).
Types
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Functions and values
| Function or value |
Description
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Full Usage:
aic k logLikelihood
Parameters:
int
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The number of parameters within the model in question.
logLikelihood : float<MeasureProduct<-logL, MeasureOne>>
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a `float` representing the minimum log-likelihood achieved for the model in question.
Returns: float
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The Akaike information criterion, a standardised index of model fit quality for models that have different numbers of parameters.
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Full Usage:
aicc n k logLikelihood
Parameters:
int
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The sample size
k : int
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The number of parameters within the model in question
logLikelihood : float<MeasureProduct<-logL, MeasureOne>>
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A `float` representing the minimum log-likelihood achieved for the model in question.
Returns: float
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The Akaike information criterion, corrected for small sample sizes. It represents standardised index of model fit quality for models that have different numbers of parameters. Your model must adhere to the following assumptions: - Univariate - Linear in parameters - Normally-distributed residuals
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Full Usage:
akaikeWeights models
Parameters:
EstimationResult<'date, 'timeunit, 'timespan> seq
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The input model results
Returns: (EstimationResult<'date, 'timeunit, 'timespan> * AkaikeWeight) seq
An (EstimationResult * float) sequence of estimation results paired to their Akaike weights.
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Akaike weights for a sequence of `EstimationResult`s.
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Full Usage:
akaikeWeightsForSet getRefCode set
Parameters:
'a -> 'b
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A function that gets a short reference code from a hypothesis.
set : ResultSet<'c, 'a, 'd, 'e, 'f> seq
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A sequence of `ResultSet`s, within each the 1 .. many results of a particular subject * hypothesis combination.
Returns: ('c * 'b * EstimationResult<'d, 'e, 'f> * AkaikeWeight) list
An `(EstimationResult * float) seq` of estimation results paired to their Akaike weights.
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Akaike weights for a result set.
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