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

Description Usage Arguments Details Value References

This funcion is deprecated, please use Alpha_spending instead.

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

`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.

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.

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).

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.