fit_mixtur: Fit the mixture model.

In mixtur: Modelling Continuous Report Visual Short-Term Memory Studies

Fit the mixture model.

Description

This is the function called by the user to fit either the two- or three- component mixture model.

Usage

fit_mixtur(
  data,
  model = "3_component",
  unit = "degrees",
  id_var = "id",
  response_var = "response",
  target_var = "target",
  non_target_var = NULL,
  set_size_var = NULL,
  condition_var = NULL,
  return_fit = FALSE
)

Arguments

data	A data frame with columns containing (at the very least) trial-level participant response and target values This data can either be in degrees (1-360 or 1-180) or radians. If the 3-component mixture model is to be fitted to the data, the data frame also needs to contain the values of all non-targets. In addition, the model can be fit to individual individual participants, individual set-sizes, and individual additional conditions; if the user wishes for this, then the data frame should have columns coding for this information.
model	A string indicating the model to be fit to the data. Currently the options are "2_component", "3_component", "slots", and "slots_averaging".
unit	A string indicating the unit of measurement in the data frame: "degrees" (measurement is in degrees, from 1 to 360); "degrees_180 (measurement is in degrees, but limited to 1 to 180); or "radians" (measurement is in radians, from pi to 2 * pi, but could also be already in the range -pi to pi).
id_var	The quoted column name coding for participant id. If the data is from a single participant (i.e., there is no id column) set to NULL.
response_var	The quoted column name coding for the participants' responses
target_var	The quoted column name coding for the target value.
non_target_var	The quoted variable name common to all columns (if applicable) storing non-target values. If the user wishes to fit the 3-component mixture model, the user should have one column coding for each non-target's value in the data frame. If there is more than one non-target, each column name should begin with a common term (e.g., the "non_target" term is common to the non-target columns "non_target_1", "non_target_2" etc.), which should then be passed to the function via the non_target_var variable.
set_size_var	The quoted column name (if applicable) coding for the set size of each response.
condition_var	The quoted column name (if applicable) coding for the condition of each response.
return_fit	If set to TRUE, the function will return the log-likelihood of the model fit, Akiakie's Information Criterion (AIC), Bayesian Information Criterion (BIC), as well as the number of trials used in the fit.

Value

Returns a data frame with best-fitting parameters per participant (if applicable), set-size (if applicable), and condition (if applicable). If return_fit was set to TRUE, the data frame will also include the log-likelihood value and information criteria of the model fit.

Source

The code for the 3-component model has been adapted from Matlab code written by Paul Bays (https://bayslab.com) published under GNU General Public License.

Examples

# load the example data
data <- bays2009_full

# fit the 3-component mixture model ignoring condition

fit <- fit_mixtur(data = data,
                  model = "3_component",
                  unit = "radians",
                  id_var = "id",
                  response_var = "response",
                  target_var = "target",
                  non_target_var = "non_target",
                  set_size_var = "set_size",
                  condition_var = NULL)

mixtur documentation built on April 6, 2023, 5:19 p.m.