SAFFRON: SAFFRON: Adaptive online FDR control
In onlineFDR: Online error control

Description Usage Arguments Details Value References See Also Examples

Implements the SAFFRON procedure for online FDR control, where SAFFRON stands for Serial estimate of the Alpha Fraction that is Futilely Rationed On true Null hypotheses, as presented by Ramdas et al. (2018). The algorithm is based on an estimate of the proportion of true null hypotheses. More precisely, SAFFRON sets the adjusted test levels based on an estimate of the amount of alpha-wealth that is allocated to testing the true null hypotheses.

SAFFRON(
  d,
  alpha = 0.05,
  gammai,
  w0,
  lambda = 0.5,
  random = TRUE,
  date.format = "%Y-%m-%d",
  discard = FALSE,
  tau.discard = 0.5
)

`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. A default is provided with γ_j proportional to 1/j^(1.6).
`w0`	Initial ‘wealth’ of the procedure, defaults to α/2. Must be between 0 and α.
`lambda`	Optional threshold for a ‘candidate’ hypothesis, must be between 0 and 1. Defaults to 0.5.
`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.
`discard`	Logical. If `TRUE` then runs the ADDIS algorithm with adaptive discarding of conservative nulls. The default is `FALSE`.
`tau.discard`	Optional threshold for hypotheses to be selected for testing. Must be between 0 and 1, defaults to 0.5. This is required if `discard=TRUE`.

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.

SAFFRON procedure provably controls FDR for independent p-values. Given an overall significance level α, we choose a sequence of non-negative non-increasing numbers γ_i that sum to 1.

SAFFRON depends on constants w_0 and λ, where w_0 satisfies 0 ≤ w_0 ≤ α and represents the initial ‘wealth’ of the procedure, and 0 < λ < 1 represents the threshold for a ‘candidate’ hypothesis. A ‘candidate’ refers to p-values smaller than λ, since SAFFRON will never reject a p-value larger than λ.

Note that FDR control also holds for the SAFFRON procedure if only the p-values corresponding to true nulls are mutually independent, and independent from the non-null p-values.

The SAFFRON procedure can lose power in the presence of conservative nulls, which can be compensated for by adaptively ‘discarding’ these p-values. This option is called by setting discard=TRUE, which is the same algorithm as ADDIS.

Further details of the SAFFRON procedure can be found in Ramdas et al. (2018).

d.out

A dataframe with the original data d (which will be reordered if there are batches and random = TRUE), the LORD-adjusted significance thresholds α_i and the indicator function of discoveries R. Hypothesis i is rejected if the i-th p-value is less than or equal to α_i, in which case R[i] = 1 (otherwise R[i] = 0).

Ramdas, A., Zrnic, T., Wainwright M.J. and Jordan, M.I. (2018). SAFFRON: an adaptive algorithm for online control of the false discovery rate. Proceedings of the 35th International Conference in Machine Learning, 80:4286-4294.

SAFFRONstar presents versions of SAFFRON for asynchronous testing, i.e. where each hypothesis test can itself be a sequential process and the tests can overlap in time.

If option discard=TRUE, SAFFRON is the same as ADDIS.

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-08, 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))

SAFFRON(sample.df, random=FALSE)

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

set.seed(1); SAFFRON(sample.df, alpha=0.1, w0=0.025)

SAFFRON(sample.df, discard=TRUE, random=FALSE)