sampled_pars {EMC2} | R Documentation |
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 class 'emc.design'
sampled_pars(
x,
group_design = NULL,
doMap = FALSE,
add_da = FALSE,
all_cells_dm = FALSE,
data = NULL
)
## S3 method for class 'emc.group_design'
sampled_pars(
x,
group_design = NULL,
doMap = FALSE,
add_da = FALSE,
all_cells_dm = FALSE,
data = NULL
)
## S3 method for class 'emc.prior'
sampled_pars(
x,
group_design = NULL,
doMap = FALSE,
add_da = FALSE,
all_cells_dm = FALSE,
data = NULL
)
## S3 method for class 'emc'
sampled_pars(
x,
group_design = NULL,
doMap = FALSE,
add_da = FALSE,
all_cells_dm = FALSE,
data = NULL
)
Arguments
x |
an |
group_design |
an |
doMap |
logical. If |
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 |
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)
[Package EMC2 version 3.2.0 Index]