hommel (version 1.6)

hommel class: Class "hommel" for storing the result of Hommel's multiple testing method.

Description

The class hommel is the output of a call to hommel. It stores the adjusted p-values of all elementary hypotheses, as well as information needed to quickly calculate other results about the underlying closed testing procedure.

Slots

These slots are not meant to be directly accessed by the user.

p:

Object of class "numeric". Stores original (unadjusted) p-values for all hypotheses.

sorter:

Object of class "integer". Stores the permutation to sort p.

jumpalpha:

Object of class "numeric". Stores the alpha-values where the function h jumps.

adjusted:

Object of class "numeric". Stores adjusted p-values for all elementary hypotheses.

simesfactor:

Object of class "numeric". The denominator of the local test that is used.

simes:

Object of class "logical". Is TRUE if the adjusted p-values are calculated based on Simes' test that assumes the Simes inequality. If FALSE if they are based on Hommel's robust test.

Methods

p.adjust

(hommel): Retrieves adjusted pvalues for all elementary hypotheses.

summary

(hommel, alpha=0.05): Summarizes the results of hommel's method for the specified level of alpha.

References

Hommel, G. (1988). A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika, 75(2), 383-386.

Meijer, R. J., Krebs, T. J., & Goeman, J. J. (2019). Hommel's procedure in linear time. Biometrical Journal, 61(1), 73-82.

Goeman, J., Meijer, R., Krebs, T., & Solari, A. (2016). Simultaneous control of all false discovery proportions in large-scale multiple hypothesis testing. arXiv preprint arXiv:1611.06739.

See Also

select, concentration.

Examples

# NOT RUN {
  #Generate a vector of pvalues
  set.seed(102)
  m <- 10
  pvalues <- c(runif(0.5*m,0,0.02), runif(0.5*m,0,1))
  
  # First step: create a hommel object.
  # Use Simes'test (simes = TRUE) or Hommel's robust test (simes = FALSE)
  hom <- hommel(pvalues, simes = TRUE)

  hom
  summary(hom)
  
  # retrieve familywise error adjusted p-values
  p.adjust(hom)
  
  # Find lower confidence bounds 
  # for the number of false null hypotheses (discoveries)
  # In any subset of interest
  discoveries(hom, 1:2)
# }