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Fit SansSouciStruct object

Source: R/SansSouciStruct-class.R
fit.SansSouciStruct.Rd

Fit SansSouciStruct object

Usage

# S3 method for SansSouciStruct
fit(
  object,
  alpha,
  p.values,
  family = c("DKWM", "HB", "trivial", "Simes", "Oracle"),
  flavor = c("tree", "partition"),
  ...
)

Arguments

object

An object of class SansSouciStruct

alpha

Target risk (JER) level

p.values

A vector of length nHyp(object),

family

A character value describing how the number of true nulls in a set is estimated. Can be either:

  • "DKWM": estimation by the Dvoretzky-Kiefer-Wolfowitz-Massart inequality (related to the Storey estimator of the proportion of true nulls), valid for independent p-values

  • "HB": estimation by the Holm-Bonferroni method, always valid

  • "trivial": dummy estimation as the the size of the set

  • "Simes": estimation via the Simes inequality, valid for positively-dependent (PRDS) p-values

  • "Oracle": true number of true null hypotheses Truth" must be available in object$input$truth

flavor

A character value which can be

  • "tree" (default value): the reference family is the entire tree structure

  • "partition": the reference family is the partition corresponding to the leaves of the tree

...

Not used

Value

A 'fitted' object of class 'SansSouciStruct'. It is a list of three elements

  • input: see SansSouciStruct

  • param: the input parameters, given as a list

  • output: a list of two elements

    • p.values: the input argument 'p.values'

    • ZL: the output of the "zeta function" associated to the input parameter 'family', see e.g. zeta.DKWM

Details

In the particular case where family=="Simes" or family=="Oracle", the return value is actually of class SansSouci and not SansSouciStruct

References

Durand, G., Blanchard, G., Neuvial, P., & Roquain, E. (2020). Post hoc false positive control for structured hypotheses. Scandinavian Journal of Statistics, 47(4), 1114-1148.

Dvoretzky, A., Kiefer, J., and Wolfowitz, J. (1956). Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator. The Annals of Mathematical Statistics, pages 642-669.

Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6 (1979), pp. 65-70.

Massart, P. (1990). The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality. The Annals of Probability, pages 1269-1283.

See also

zeta.DKWM zeta.HB, zeta.tricial

Examples

s <- 100
q <- 7
m <- s*2^q
obj <- SansSouciDyadic(m, leaf_size = s, direction = "top-down")

mu <- gen.mu.leaves(m = m, K1 = 8, d = 0.9, grouped = TRUE, 
  setting = "const", barmu = 3, leaf_list = obj$input$leaves)
pvalues <- gen.p.values(m = m, mu = mu, rho = 0)

alpha <- 0.05
S1 <- which(mu != 0)

res_DKWM <- fit(obj, alpha, pvalues, "DKWM")
predict(res_DKWM, S = S1, what = "FP")
#> [1] 348

res_Simes <- fit(obj, alpha, pvalues, "Simes")
predict(res_Simes, S = S1, what = "FP")
#> [1] 584