pairs_posterior: Plot Within-Chain Correlations

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Plot Within-Chain Correlations

Description

Plots within-chain parameter correlations (upper triangle) and corresponding scatterplots (lower triangle) to visualize parameter sloppiness.

Usage

pairs_posterior(
  emc,
  selection = "alpha",
  scale_subjects = TRUE,
  do_plot = TRUE,
  N = 500,
  ...
)

Arguments

emc	An emc object
selection	A Character string. Indicates which parameter type to plot (alpha, mu, variance, covariance, correlation).
scale_subjects	Boolean. To standardize each participant with selection = "alpha", by subtracting the mean and divding by the standard deviation. This ensures the plot has every participant on the same scale.
do_plot	Boolean. Whether to plot the pairs plot, if FALSE, only the correlations are returned.
N	Integer for maximum number of iterations used (defaults to 500). If number of samples in stage or selection exceeds N, a random subset will be taken of size N
...	Optional arguments that can be passed to get_pars

Details

If selection = alpha the parameter chains are concatenated across participants, (after standardizing if scale_subjects = TRUE) and then correlated.

Value

Invisibly returns a matrix with the correlations between the parameters.

Examples

# Plot the sloppiness for the individual-level subjects
pairs_posterior(samples_LNR, selection = "alpha")

# We can also choose group-level parameters and subsets of the parameter space 
pairs_posterior(samples_LNR, use_par = c("m", "t0"), selection = "sigma2")

EMC2 documentation built on April 11, 2025, 5:50 p.m.