null.nig.fit: null.nig.fit

In npetraco/fdrID: Computes local false discovery rate probabilities using JAGS, Stan and code from Efron, Turnbull and Narasimhan locfdr

Description

Normal inverse gaussian fit for null similarity scores.

Usage

null.nig.fit(score.null.vec, max.iter = 1000, standardizeQ = FALSE,
  plotQ = FALSE)

Arguments

score.null.vec	Vector of null (non-match) similarity scores or transformed similarity scores
standardizeQ	Whether or not to standardize the null scores
plotQ	Diagnostic plots?

Details

The normal inverse gaussian distribution is a flexible four parameter distribution which often gives a good fit to the null log similarity scores when these scores are on a scale of 0 to 1. Other than standardization, this function assumes that if the user wants the scores to be transformed, they've transformed them. This routine calls the fit.NIGuv function from the ghyp package.

Value

list with the fitted parameters, fit info and chi-square goodness of fit test results

References

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Examples

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npetraco/fdrID documentation built on May 23, 2019, 9:33 p.m.