Alpha_spending {onlineFDR} | R Documentation

## Alpha-spending for online FWER control

### Description

Implements online FWER control using a Bonferroni-like test.

### Usage

    Alpha_spending(
      d,
      alpha = 0.05,
      gammai,
      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.
`gammai` | Optional vector of γ_i, where hypothesis i is rejected if the i-th p-value is less than or equal to α γ_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

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.

Alpha-spending provides strong FWER control 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 γ_i such that they sum to 1. Hypothesis i is rejected if the i-th p-value is less than or equal to α γ_i.

Note that the procedure controls the generalised familywise error rate (k-FWER) for k > 1 if α is replaced by min(1, kα).

### Value

`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.

Tian, J. and Ramdas, A. (2021). Online control of the familywise error rate. Statistical Methods for Medical Research (to appear), https://arxiv.org/abs/1910.04900.

### Examples

    sample.df <- data.frame(
    id = c('A15432', 'B90969', 'C18705', 'B49731', 'E99902',
        'C38292', 'A30619', 'D46627', 'E29198', 'A41418',
        'D51456', 'C88669', 'E03673', 'A63155', 'B66033'),
    date = as.Date(c(rep('2014-12-01',3),
                    rep('2015-09-21',5),
                    rep('2016-05-19',2),
                    '2016-11-12',
                    rep('2017-03-27',4))),
    pval = c(2.90e-17, 0.06743, 0.01514, 0.08174, 0.00171,
            3.60e-05, 0.79149, 0.27201, 0.28295, 7.59e-08,
            0.69274, 0.30443, 0.00136, 0.72342, 0.54757))

    set.seed(1); Alpha_spending(sample.df)

    Alpha_spending(sample.df, random=FALSE)

    set.seed(1); Alpha_spending(sample.df, alpha=0.1)

[Package onlineFDR version 2.2.0 Index]