bmm: Fit Bayesian Measurement Models

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

Fit Bayesian Measurement Models

Description

Fit a Bayesian measurement model using brms as a backend interface to Stan.

Usage

bmm(
  formula,
  data,
  model,
  prior = NULL,
  sort_data = getOption("bmm.sort_data", "check"),
  silent = getOption("bmm.silent", 1),
  backend = getOption("brms.backend", NULL),
  file = NULL,
  file_compress = TRUE,
  file_refit = getOption("bmm.file_refit", FALSE),
  ...
)

fit_model(
  formula,
  data,
  model,
  prior = NULL,
  sort_data = getOption("bmm.sort_data", "check"),
  silent = getOption("bmm.silent", 1),
  backend = getOption("brms.backend", NULL),
  ...
)

Arguments

formula	An object of class bmmformula. A symbolic description of the model to be fitted.
data	An object of class data.frame, containing data of all variables used in the model. The names of the variables must match the variable names passed to the bmmodel object for required argurments.
model	A description of the model to be fitted. This is a call to a bmmodel such as mixture3p() function. Every model function has a number of required arguments which need to be specified within the function call. Call supported_models() to see the list of supported models and their required arguments
prior	One or more brmsprior objects created by brms::set_prior() or related functions and combined using the c method or the + operator. See also default_prior() for more help. Not necessary for the default model fitting, but you can provide prior constraints to model parameters
sort_data	Logical. If TRUE, the data will be sorted by the predictor variables for faster sampling. If FALSE, the data will not be sorted, but sampling will be slower. If "check" (the default), bmm() will check if the data is sorted, and ask you via a console prompt if it should be sorted. You can set the default value for this option using global ⁠options(bmm.sort_data = TRUE/FALSE/"check)⁠)⁠or via⁠bmm_options(sort_data)'
silent	Verbosity level between 0 and 2. If 1 (the default), most of the informational messages of compiler and sampler are suppressed. If 2, even more messages are suppressed. The actual sampling progress is still printed. Set refresh = 0 to turn this off as well. If using backend = "rstan" you can also set open_progress = FALSE to prevent opening additional progress bars.
backend	Character. The backend to use for fitting the model. Can be "rstan" or "cmdstanr". If NULL (the default), "cmdstanr" will be used if the cmdstanr package is installed, otherwise "rstan" will be used. You can set the default backend using global options(brms.backend = "rstan"/"cmdstanr")
file	Either NULL or a character string. If a string, the fitted model object is saved via saveRDS in a file named after the string. The ⁠.rds extension is added automatically. If the file already exists, ⁠bmm⁠will load and return the saved model object. Unless you specify the⁠file_refit⁠argument as well, the existing files won't be overwritten, you have to manually remove the file in order to refit and save the model under an existing file name. The file name is stored in the⁠bmmfit' object for later usage. If the directory of the file does not exist, it will be created.
file_compress	Logical or a character string, specifying one of the compression algorithms supported by saveRDS when saving the fitted model object.
file_refit	Logical. Modifies when the fit stored via the file argument is re-used. Can be set globally for the current R session via the "bmm.file_refit" option (see options). If TRUE (the default), the model is re-used if the file exists. If FALSE, the model is re-fitted. Note that unlike brms, there is no "on_change" option
...	Further arguments passed to brms::brm() or Stan. See the description of brms::brm() for more details

Value

An object of class brmsfit which contains the posterior draws along with many other useful information about the model. Use methods(class = "brmsfit") for an overview on available methods.

Supported Models

The following models are supported:

• imm(resp_error, nt_features, nt_distances, set_size, regex, version)

• mixture2p(resp_error)

• mixture3p(resp_error, nt_features, set_size, regex)

• sdm(resp_error, version)

Type ?modelname to get information about a specific model, e.g. ?imm

bmmformula syntax

see this online article for a detailed description of the syntax and how it differs from the syntax for brmsformula

Default priors, Stan code and Stan data

For more information about the default priors in bmm and about who to extract the Stan code and data generated by bmm and #' brms, see the online article.

Miscellaneous

Type help(package=bmm) for a full list of available help topics.

fit_model() is a deprecated alias for bmm().

References

Frischkorn, G. T., & Popov, V. (2023). A tutorial for estimating mixture models for visual working memory tasks in brms: Introducing the Bayesian Measurement Modeling (bmm) package for R. https://doi.org/10.31234/osf.io/umt57

See Also

supported_models(), brms::brm(), default_prior(), bmmformula(), stancode(), standata()

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')

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