bonfInfinite: Online FDR control based on a Bonferroni-like test

Description Usage Arguments Details Value References

View source: R/bonfInfinite.R

Description

This funcion is deprecated, please use Alpha_spending instead.

Usage

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bonfInfinite(
  d,
  alpha = 0.05,
  alphai,
  random = TRUE,
  date.format = "%Y-%m-%d"
)

Arguments

d

Either a vector of p-values, or a dataframe with three columns: an identifier (‘id’), date (‘date’) and p-value (‘pval’). If no column of dates is provided, then the p-values are treated as being ordered sequentially with no batches.

alpha

Overall significance level of the FDR procedure, the default is 0.05.

alphai

Optional vector of α_i, where hypothesis i is rejected if the i-th p-value is less than or equal to α_i. A default is provided as proposed by Javanmard and Montanari (2018), equation 31.

random

Logical. If TRUE (the default), then the order of the p-values in each batch (i.e. those that have exactly the same date) is randomised.

date.format

Optional string giving the format that is used for dates.

Details

Implements online FDR control using a Bonferroni-like test.

The function takes as its input either a vector of p-values, or a dataframe with three columns: an identifier (‘id’), date (‘date’) and p-value (‘pval’). The case where p-values arrive in batches corresponds to multiple instances of the same date. If no column of dates is provided, then the p-values are treated as being ordered sequentially with no batches.

The procedure controls FDR for a potentially infinite stream of p-values by using a Bonferroni-like test. Given an overall significance level α, we choose a (potentially infinite) sequence of non-negative numbers α_i such that they sum to α. Hypothesis i is rejected if the i-th p-value is less than or equal to α_i.

Value

d.out

A dataframe with the original data d (which will be reordered if there are batches and random = TRUE), the adjusted signifcance thresholds alphai and the indicator function of discoveries R, where R[i] = 1 corresponds to hypothesis i being rejected (otherwise R[i] = 0).

References

Javanmard, A. and Montanari, A. (2018) Online Rules for Control of False Discovery Rate and False Discovery Exceedance. Annals of Statistics, 46(2):526-554.


onlineFDR documentation built on Nov. 8, 2020, 6:35 p.m.