bmm (version 1.2.0)

m3dist: Distribution functions for the Memory Measurement Model (M3)

Description

Density and random generation functions for the memory measurement model. Please note that these functions are currently not vectorized.

Usage

dm3(
  x,
  pars,
  m3_model,
  act_funs = construct_m3_act_funs(m3_model, warnings = FALSE),
  log = TRUE,
  ...
)
rm3(
  n,
  size,
  pars,
  m3_model,
  act_funs = construct_m3_act_funs(m3_model, warnings = FALSE),
  ...
)

Value

dm3 gives the density of the memory measurement model, and rm3

gives the random generation function for the memory measurement model.

Arguments

x

Integer vector of length K where K is the number of response categories and each value is the number of observed responses per category

pars

A named vector of parameters of the memory measurement model

m3_model

A bmmodel object specifying the m3 model that densities or random samples should be generated for

act_funs

A bmmformula object specifying the activation functions for the different response categories for the "custom" version of the M3. The default will attempt to construct the standard activation functions for the "ss" and "cs" model version. For a custom m3 model you need to specify the act_funs argument manually

log

Logical; if TRUE (default), values are returned on the log scale.

...

can be used to pass additional variables that are used in the activation functions, but not parameters of the model

n

Integer. Number of observations to generate data for

size

The total number of observations in all categories

References

Oberauer, K., & Lewandowsky, S. (2019). Simple measurement models for complex working-memory tasks. Psychological Review, 126(6), 880–932. https://doi.org/10.1037/rev0000159

Examples

  model <- m3(
   resp_cats = c("corr", "other", "npl"),
   num_options = c(1, 4, 5),
   choice_rule = "simple",
   version = "ss"
 )
 dm3(x = c(20, 10, 10), pars = c(a = 1, b = 1, c = 2), m3_model = model)