view_rmcorr_shiny: Interactive Shiny Viewer for Repeated-Measures Correlation

In matrixCorr: Collection of Correlation and Association Estimators

Interactive Shiny Viewer for Repeated-Measures Correlation

Description

Launches a dedicated Shiny gadget for repeated-measures correlation matrix objects of class "rmcorr_matrix". The viewer combines the correlation heatmap with a pairwise scatterplot panel that rebuilds the corresponding two-variable "rmcorr" fit for user-selected variables.

Usage

view_rmcorr_shiny(x, title = NULL, default_max_vars = 40L)

Arguments

x	An object of class "rmcorr_matrix" or a named list of such objects.
title	Optional character title shown at the top of the gadget.
default_max_vars	Integer; maximum number of variables pre-selected in the heatmap view when the app opens. Defaults to 40.

Details

This helper requires the shiny and shinyWidgets packages at runtime and will optionally use plotly for the heatmap when available. The pairwise panel reuses the package's regular plot.rmcorr() method, so the Shiny scatterplot matches the standard pairwise repeated-measures correlation plot. To rebuild pairwise fits from a returned "rmcorr_matrix" object, the matrix must have been created with keep_data = TRUE.

Value

Invisibly returns NULL; the function is called for its side effect of launching a Shiny gadget.

Examples

if (interactive()) {
  set.seed(2026)
  n_subjects <- 20
  n_rep <- 4
  subject <- rep(seq_len(n_subjects), each = n_rep)
  subj_eff_x <- rnorm(n_subjects, sd = 1.5)
  subj_eff_y <- rnorm(n_subjects, sd = 2.0)
  within_signal <- rnorm(n_subjects * n_rep)

  dat <- data.frame(
    subject = subject,
    x = subj_eff_x[subject] + within_signal + rnorm(n_subjects * n_rep, sd = 0.2),
    y = subj_eff_y[subject] + 0.8 * within_signal + rnorm(n_subjects * n_rep, sd = 0.3),
    z = subj_eff_y[subject] - 0.4 * within_signal + rnorm(n_subjects * n_rep, sd = 0.4)
  )

  fit_mat <- rmcorr(
    dat,
    response = c("x", "y", "z"),
    subject = "subject",
    keep_data = TRUE
  )
  view_rmcorr_shiny(fit_mat)
}

matrixCorr documentation built on April 18, 2026, 5:06 p.m.