Create formula for predicting parameters of a bmmodel

This function is used to specify the formulas predicting the different parameters of a bmmodel.

Usage

bmmformula(...)

bmf(...)

Arguments

...

Formulas for predicting a bmmodel parameter. Each formula for a parameter should be specified as a separate argument, separated by commas

Value

A list of formulas for each parameters being predicted

General formula structure

The formula argument accepts formulas of the following syntax:

parameter ~ fixed_effects + (random_effects | grouping_variable)

bmm formulas are built on brms formulas and function in nearly the same way, so you can use most of the brms formula syntax. The main differences is that in bmm formulas, the response variable is not specified in the formula. Instead, each parameter of the model is explicitly specified as the left-hand side of the formula. In brms, the response variable is always specified as the left-hand side of the first formula, which implicitly means that any predictors in the first formula are predictors of the mu parameter of the model. In general, measurement models do not all have a mu parameter, therefore it is more straigthforward to explicitely predict each parameter of the model.

For example, in the following brms formula for the drift diffusion model, the first line corresponds to the drift rate parameter, but this is not explicitely stated.

      brmsformula(rt | dec(response) ~ condition + (condition | id),
                  bs ~ 1 + (1 | id),
                  ndt ~ 1 + (1 | id),
                  bias ~ 1 + (1 | id))

In bmm, the same formula would be written as:

      bmmformula(drift ~ condition + (condition | id),
                 bs ~ 1 + (1 | id),
                 ndt ~ 1 + (1 | id),
                 bias ~ 1 + (1 | id))

and the rt and response variables would be specified in the model argument of the bmm() function.

Aside from that, the bmm formula syntax is the same as the brms formula syntax. For more information on the brms formula syntax, see brms::brmsformula().

You can also use the bmf() function as a shorthand for bmmformula().

Examples

imm_formula <- bmmformula(
  c ~ 0 + set_size + (0 + set_size | id),
  a ~ 1,
  kappa ~ 0 + set_size + (0 + set_size | id)
)

# or use the shorter alias 'bmf'
imm_formula2 <- bmf(
  c ~ 0 + set_size + (0 + set_size | id),
  a ~ 1,
  kappa ~ 0 + set_size + (0 + set_size | id)
)
identical(imm_formula, imm_formula2)
#> [1] TRUE