Distribution functions for the Memory Measurement Model (M3)

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),
  ...
)

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

Value

dm3 gives the density of the memory measurement model, and rm3 gives the random generation function for the memory measurement model.

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)
#> [1] -10.93765