extract_empirical_clusters {jlmerclusterperm}

Detect largest clusters from a time sequence of predictor statistics

Description

Detect largest clusters from a time sequence of predictor statistics

Usage

extract_empirical_clusters(
  empirical_statistics,
  threshold,
  binned = FALSE,
  top_n = Inf
)

Arguments

empirical_statistics

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

threshold

The threshold value that the statistic must pass to contribute to cluster mass. Interpretation differs on the choice of statistic (more below):

• If statistic = "t", the threshold for t-value (beta/std.err) from the regression model.

• If statistic = "chisq", the threshold for the p-value of chi-squared statistics from likelihood ratio tests.

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.

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.

Value

An empirical_clusters object.

See Also

compute_timewise_statistics()

Examples

library(dplyr, warn.conflicts = FALSE)

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

# Empirical clusters are derived from the timewise statistics
empirical_statistics <- compute_timewise_statistics(spec)
empirical_clusters <- extract_empirical_clusters(empirical_statistics, threshold = 2)
empirical_clusters

# Collect as dataframe with `tidy()`
empirical_clusters_df <- tidy(empirical_clusters)
empirical_clusters_df

# Changing the `threshold` value identifies different clusters
extract_empirical_clusters(empirical_statistics, threshold = 1)

# A predictor can have zero or multiple clusters associated with it
extract_empirical_clusters(empirical_statistics, threshold = 3)