EMC2 (version 3.2.0)

sampled_pars: 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,
  group_design = NULL,
  doMap = FALSE,
  add_da = FALSE,
  all_cells_dm = FALSE,
  data = NULL
)
# S3 method for emc.design
sampled_pars(
  x,
  group_design = NULL,
  doMap = FALSE,
  add_da = FALSE,
  all_cells_dm = FALSE,
  data = NULL
)
# S3 method for emc.group_design
sampled_pars(
  x,
  group_design = NULL,
  doMap = FALSE,
  add_da = FALSE,
  all_cells_dm = FALSE,
  data = NULL
)
# S3 method for emc.prior
sampled_pars(
  x,
  group_design = NULL,
  doMap = FALSE,
  add_da = FALSE,
  all_cells_dm = FALSE,
  data = NULL
)
# S3 method for emc
sampled_pars(
  x,
  group_design = NULL,
  doMap = FALSE,
  add_da = FALSE,
  all_cells_dm = FALSE,
  data = NULL
)

Value

Named vector.

Arguments

x

an emc.design object made with design() or an emc object.

group_design

an emc.group_design object made with group_design()

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

data

A data frame to be included for accurate covariate mapping in summary.design

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)