plot.emc.prior: Plot a prior

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Plot a prior

Description

Takes a prior object and plots the selected implied prior

Usage

## S3 method for class 'emc.prior'
plot(
  x,
  selection = "mu",
  do_plot = TRUE,
  covariates = NULL,
  layout = NA,
  N = 50000,
  ...
)

Arguments

x	An emc_prior element
selection	A Character string. Indicates which parameter type to use (e.g., alpha, mu, sigma2, correlation).
do_plot	Boolean. If FALSE will only return prior samples and omit plotting.
covariates	dataframe/functions as specified by the design
layout	A vector indicating which layout to use as in par(mfrow = layout). If NA, will automatically generate an appropriate layout.
N	Integer. How many prior samples to draw
...	Optional arguments that can be passed to get_pars, histogram, plot.default (see par()), or arguments required for the types of models e.g. n_factors for type = "factor"

Value

An invisible mcmc.list object with prior samples of the selected type

Examples

# First define a design for the model
design_DDMaE <- design(data = forstmann,model=DDM,
                           formula =list(v~0+S,a~E, t0~1, s~1, Z~1, sv~1, SZ~1),
                           constants=c(s=log(1)))
# Then set up a prior using make_prior
p_vector=c(v_Sleft=-2,v_Sright=2,a=log(1),a_Eneutral=log(1.5),a_Eaccuracy=log(2),
          t0=log(.2),Z=qnorm(.5),sv=log(.5),SZ=qnorm(.5))
psd <- c(v_Sleft=1,v_Sright=1,a=.3,a_Eneutral=.3,a_Eaccuracy=.3,
          t0=.4,Z=1,sv=.4,SZ=1)
# Here we left the variance prior at default
prior_DDMaE <- prior(design_DDMaE,mu_mean=p_vector,mu_sd=psd)
# Now we can plot all sorts of (implied) priors
plot(prior_DDMaE, selection = "mu", N = 1e3)
plot(prior_DDMaE, selection = "mu", mapped = FALSE, N=1e3)
# We can also plot the implied prior on the participant level effects.
plot(prior_DDMaE, selection = "alpha", col = "green", N = 1e3)

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