fdr (pyleoclim.utils.correlation.fdr)

pyleoclim.utils.correlation.fdr(pvals, qlevel=0.05, method='original', adj_method=None, adj_args={})[source]

Determine significance based on the FDR approach

The false discovery rate is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. Translated from fdr.R by Dr. Chris Paciorek

Parameters

  • pvals (list or array) – A vector of p-values on which to conduct the multiple testing.

  • qlevel (float) – The proportion of false positives desired.

  • method ({'original', 'general'}) –

    Method for performing the testing.

    • ’original’ follows Benjamini & Hochberg (1995);

    • ’general’ is much more conservative, requiring no assumptions on the p-values (see Benjamini & Yekutieli (2001)).

    ’original’ is recommended, and if desired, using ‘adj_method=”mean”’ to increase power.

  • adj_method ({'mean', 'storey', 'two-stage'}) –

    Method for increasing the power of the procedure by estimating the proportion of alternative p-values.

    • ’mean’, the modified Storey estimator in Ventura et al. (2004)

    • ’storey’, the method of Storey (2002)

    • ’two-stage’, the iterative approach of Benjamini et al. (2001)

  • adj_args (dict) – Arguments for adj_method; see prop_alt() for description, but note that for “two-stage”, qlevel and fdr_method are taken from the qlevel and method arguments for fdr()

Returns

fdr_res – A vector of the indices of the significant tests; None if no significant tests

Return type

array or None

References