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{{alias}}( pvals, method[, comparisons] )
Adjusts supplied p-values for multiple comparisons via a specified method.
The `method` parameter can be one of the following values:
- **bh**: Benjamini-Hochberg procedure controlling the False Discovery
Rate (FDR).
- **bonferroni**: Bonferroni correction fixing the family-wise error rate
by multiplying the p-values with the number of comparisons. The Bonferroni
correction is usually a too conservative adjustment compared to the others.
- **by**: Procedure by Benjamini & Yekutieli for controlling the False
Discovery Rate (FDR) under dependence.
- **holm**: Hommel's method controlling family-wise error rate. It is
uniformly more powerful than the Bonferroni correction.
- **hommel**: Hommel's method, which is valid when hypothesis tests are
independent. It is more expensive to compute than the other methods.
By default, the number of comparisons for which the p-values should be
corrected is equal to the number of provided p-values. Alternatively, it is
possible to set `comparisons` to a number greater than the length of
`pvals`. In that case, the methods assume `comparisons - pvals.length`
unobserved p-values that are greater than all observed p-values (for Holm's
method and the Bonferroni correction) or equal to `1` for the remaining
methods.
Parameters
----------
pvals: Array<number>
P-values to be adjusted.
method: string
Correction method.
comparisons: integer (optional)
Number of comparisons. Default value: `pvals.length`.
Returns
-------
out: Array<number>
Array containing the corrected p-values.
Examples
--------
> var pvalues = [ 0.008, 0.03, 0.123, 0.6, 0.2 ];
> var out = {{alias}}( pvalues, 'bh' )
[ 0.04, 0.075, ~0.205, 0.6, 0.25 ]
> out = {{alias}}( pvalues, 'bonferroni' )
[ 0.04, 0.15, 0.615, 1.0, 1.0 ]
> out = {{alias}}( pvalues, 'by' )
[ ~0.457, ~0.856, 1.0, 1.0, 1.0 ]
> out = {{alias}}( pvalues, 'holm' )
[ 0.2, 0.6, 1.0, 1.0, 1.0 ]
> out = {{alias}}( pvalues, 'hommel' )
[ 0.16, 0.6, 1.0, 1.0, 1.0 ]
See Also
--------
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