summary.bmmfit: Create a summary of a fitted model represented by a 'bmmfit'...

In bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

Create a summary of a fitted model represented by a bmmfit object

Description

Create a summary of a fitted model represented by a bmmfit object

Usage

## S3 method for class 'bmmfit'
summary(
  object,
  priors = FALSE,
  prob = 0.95,
  robust = FALSE,
  mc_se = FALSE,
  ...,
  backend = "bmm"
)

Arguments

object	An object of class brmsfit.
priors	Logical; Indicating if priors should be included in the summary. Default is FALSE.
prob	A value between 0 and 1 indicating the desired probability to be covered by the uncertainty intervals. The default is 0.95.
robust	If FALSE (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If TRUE, the median and the median absolute deviation (MAD) are applied instead.
mc_se	Logical; Indicating if the uncertainty in Estimate caused by the MCMC sampling should be shown in the summary. Defaults to FALSE.
...	Other potential arguments
backend	Choose whether to display the bmm summary method (default), or to display the brms summary method.

Value

A list of class bmmsummary containing the summary of the model's parameters, the model formula, the model, and the data used to fit the model.

Note

You can turn off the color output by setting the option options(bmm.color_summary = FALSE) or bmm_options(color_summary = FALSE)

See Also

summary.brmsfit

Examples

# generate artificial data from the Signal Discrimination Model
dat <- data.frame(y = rsdm(2000))

# define formula
ff <- bmmformula(c ~ 1, kappa ~ 1)

# fit the model
fit <- bmm(
  formula = ff,
  data = dat,
  model = sdm(resp_error = "y"),
  cores = 4,
  backend = "cmdstanr"
)

# summary of the model
summary(fit)

bmm documentation built on May 29, 2024, 11:52 a.m.