multiple_testing_correction: Main analysis function

In jcortesr/PerMuTe: Permutation Multiple Testing methods in R

Main analysis function

Description

Main analysis function

Usage

multiple_testing_correction(
  data,
  fx,
  method = "all",
  nperm = 1000,
  alpha_local = 0.05,
  alpha_global = NULL,
  null_distribution = "normal",
  seed = NULL,
  block_size = NULL,
  verbose = TRUE
)

Arguments

data	Array containing data. First two dimensions are assumed to be the spatial dimensions, third dimension must be the variable. Note function fails if data is not as specified.
fx	function to be applied at each grid cell. Should be self sufficient - no extra arguments needed besides the time series. Should return only the test statistic
method	a string of characters indicating which correction for multiple testing to use. Defaults to c("maxT","stcs"). Additional options are "bonferroni", "bh", "by", "holmes", "hochberg". "bh" is the Benjamini-Hochberg method, also known as the false discovery rate, "by" is the Benjamini-Yekutieli method. See references for more information.
alpha_local	Significance level for the hypthesis test performed at each grid cell.
alpha_global	significance level to be applied to the permutation methods. This controls the overall probability of a false positive among all the data at the specified alpha. Recommended to be at the same significance level as the thr. Defaults to alpha_local.
null_distribution	either "normal" or "t". Used to estimate the threshold of significance for the test statistic. Defaults to normal
seed	seed to be fed into set.seed function
block_size	Desired block size for block permutation. Useful for serially correlated data.
verbose	Counter returning when the function is done with 10 function calls

Value

A named list of matrices: the first two contain the test statistic and the p-values for each grid cell, afterwards each matrix contains true/false for every grid cell in the input data, indicating significance /nonsignificance according to the corresponding method

References

Cortés, J., Mahecha, M., Reichstein, M. et al. Accounting for multiple testing in the analysis of spatio-temporal environmental data. Environ Ecol Stat 27, 293–318 (2020). https://doi.org/10.1007/s10651-020-00446-4

jcortesr/PerMuTe documentation built on July 31, 2023, 8:03 a.m.