credible: Posterior Credible Interval Tests

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Posterior Credible Interval Tests

Description

Modeled after t.test, returns the credible interval of the parameter or test and what proportion of the posterior distribution (or the difference in posterior distributions in case of a two sample test) overlaps with mu. For a one sample test provide x and for two sample also provide y. Note that for comparisons within one model, we recommend using hypothesis() if the priors were well chosen.

Usage

## S3 method for class 'emc'
credible(
  x,
  x_name = NULL,
  x_fun = NULL,
  x_fun_name = "fun",
  selection = "mu",
  y = NULL,
  y_name = NULL,
  y_fun = NULL,
  y_fun_name = "fun",
  x_subject = NULL,
  y_subject = NULL,
  mu = 0,
  alternative = c("less", "greater")[1],
  probs = c(0.025, 0.5, 0.975),
  digits = 2,
  p_digits = 3,
  print_table = TRUE,
  ...
)

credible(x, ...)

Arguments

x	An emc object
x_name	A character string. Name of the parameter to be tested for x
x_fun	Function applied to the MCMC chains to create variable to be tested.
x_fun_name	Name to give to quantity calculated by x_fun
selection	A character string designating parameter type (e.g. alpha or covariance)
y	A second emc object
y_name	A character string. Name of the parameter to be tested for y
y_fun	Function applied to the MCMC chains to create variable to be tested.
y_fun_name	Name to give to quantity calculated by y_fun
x_subject	Integer or name selecting a subject
y_subject	Integer or name selecting a subject
mu	Numeric. NULL value for single sample test if y is not supplied (default 0)
alternative	less or greater determining direction of test probability
probs	Vector defining quantiles to return.
digits	Integer, significant digits for estimates in printed results
p_digits	Integer, significant digits for probability in printed results
print_table	Boolean (defaults to TRUE) for printing results table
...	Additional optional arguments that can be passed to get_pars

Value

Invisible results table with no rounding.

Examples

{
# Run a credible interval test (Bayesian ''t-test'')
credible(samples_LNR, x_name = "m")
# We can also compare between two sets of emc objects

# # Now without a ~ E
# design_null <- design(data = forstmann,model=DDM,
#                            formula =list(v~0+S,a~1, t0~1, s~1, Z~1, sv~1, SZ~1),
#                            constants=c(s=log(1)))
#
# null_model <- make_emc(forstmann, design_null)
# null_model <- fit(null_model)
# credible(x = null_model, x_name = "a", y = full_model, y_name = "a")
#
# # Or provide custom functions:
# credible(x = full_model, x_fun = function(d) d["a_Eaccuracy"] - d["a_Eneutral"])
}

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