sampled_pars: Get Model Parameters from a Design

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Get Model Parameters from a Design

Description

Makes a vector with zeroes, with names and length corresponding to the model parameters of the design.

Usage

sampled_pars(
  x,
  model = NULL,
  doMap = TRUE,
  add_da = FALSE,
  all_cells_dm = FALSE
)

## S3 method for class 'emc.design'
sampled_pars(
  x,
  model = NULL,
  doMap = TRUE,
  add_da = FALSE,
  all_cells_dm = FALSE
)

## S3 method for class 'emc.prior'
sampled_pars(
  x,
  model = NULL,
  doMap = TRUE,
  add_da = FALSE,
  all_cells_dm = FALSE
)

## S3 method for class 'emc'
sampled_pars(
  x,
  model = NULL,
  doMap = TRUE,
  add_da = FALSE,
  all_cells_dm = FALSE
)

Arguments

x	an emc.design object made with design() or an emc object.
model	a model list. Defaults to the model specified in the design list.
doMap	logical. If TRUE will also include an attribute map with the design matrices that perform the mapping back to the design
add_da	Boolean. Whether to include the relevant data columns in the map attribute
all_cells_dm	Boolean. Whether to include all levels of a factor in the mapping attribute, even when one is dropped in the design

Value

Named vector.

Examples

# First define a design
design_DDMaE <- design(data = forstmann,model=DDM,
                           formula =list(v~0+S,a~E, t0~1, s~1, Z~1, sv~1, SZ~1),
                           constants=c(s=log(1)))
# Then for this design get which cognitive model parameters are sampled:
sampled_pars(design_DDMaE)

EMC2 documentation built on April 11, 2025, 5:50 p.m.