walk_threshold_steps {jlmerclusterperm}

Test the probability of cluster-mass statistics over a range of threshold values

Description

Test the probability of cluster-mass statistics over a range of threshold values

Usage

walk_threshold_steps(
  empirical_statistics,
  null_statistics,
  steps,
  top_n = Inf,
  binned = FALSE,
  add1 = TRUE,
  progress = TRUE
)

Arguments

empirical_statistics

A predictor-by-time matrix of empirical timewise statistics.

null_statistics

A simulation-by-time-by-predictor 3D array of null (permuted) timewise statistics.

steps

A vector of threshold values to test

top_n

How many clusters to return, in the order of the size of the cluster-mass statistic. Defaults to Inf which return all detected clusters.

binned

Whether the data has been aggregated/collapsed into time bins. Defaults to FALSE, which requires a cluster to span at least two time points. If TRUE, allows length-1 clusters to exist.

add1

Whether to add 1 to the numerator and denominator when calculating the p-value. Use TRUE to effectively count the observed statistic as part of the permuted null distribution (recommended with larger nsim prior to publishing results).

progress

Whether to display a progress bar

Value

A data frame of predictor clusters-mass statistics by threshold.

Examples

# Specification object
spec <- make_jlmer_spec(
  weight ~ 1 + Diet, subset(ChickWeight, Time <= 20),
  subject = "Chick", time = "Time"
)
spec

# Compute timewise statistics for the observed and permuted data
empirical_statistics <- compute_timewise_statistics(spec)
null_statistics <- permute_timewise_statistics(spec, nsim = 100)

# Test cluster mass/probability under different threshold values
walk_threshold_steps(empirical_statistics, null_statistics, steps = 1:3,
                     progress = FALSE)