BuildModel {ggdmcModel} | R Documentation |
Build a model object
Description
The function performs a series of syntax checks to ensure the user enters strings/values conforming the C++ internal setting.
Usage
BuildModel(
p_map = list(A = "1", B = "1", mean_v = "M", sd_v = "1", st0 = "1", t0 = "1"),
accumulators = c("r1", "r2"),
factors = list(S = c("s1", "s2")),
match_map = list(M = list(s1 = "r1", s2 = "r2")),
constants = c(sd_v = 1, st0 = 0),
type = "lba",
print_method = "head",
verbose = TRUE
)
Arguments
p_map |
Descibes the association between the parameter and the experimental factor. |
accumulators |
Specifies the response names and their levels. |
factors |
Specifies a list of factors along with their levels or conditions. |
match_map |
Maps stimulus conditions to response levels, indicating correctness. |
constants |
Allows the user to fix certain model parameters at constant values. |
type |
The model type used in the package, "fastdm", "hyper", or "lba". |
print_method |
a string indicating how you want the function to print model information.
. Default to |
verbose |
Logical; if |
Value
A S4 'model' object containing the following slots:
-
parameter_map
Stores the assocation between model parameters and the factors. -
accumulators
Names of internal accumulators or manifested responses. -
factors
Names of the factors. -
match_map
Mapping between stimuli and responses. -
constants
Specifies which model parameters are fixed to constant values. -
cell_names
Names of the experimental conditions aora a cells. -
parameter_x_condition_names
Parameter names after associated with conditions. -
model_boolean
A 3D Boolean array guiding the allocation of model parameters to conditions. -
pnames
Names of the model parameter associated with conditons. -
npar
Numbers of parameters. -
type
a string indicating the model type.
Examples
## A diffusion decision model
model <- BuildModel(
p_map = list(
a = c("S", "COLOUR"), v = c("NOISE"), z = "1", d = "1", sz = "1", sv = "1",
t0 = "1", st0 = "1", s = "1", precision = "1"
),
match_map = list(M = list(left = "z_key", right = "x_key")),
factors = list(
S = c("left", "right"), COLOUR = c("red", "blue"),
NOISE = c("high", "moderate", "low")
),
constants = c(d = 0, s = 1, st0 = 0, sv = 0, precision = 3),
accumulators = c("z_key", "x_key"),
type = "fastdm"
)
## A LBA model
model <- BuildModel(
p_map = list(
A = "1", B = c("S", "COLOR"), t0 = "1", mean_v = c("NOISE", "M"),
sd_v = "M", st0 = "1"
),
match_map = list(M = list(left = "z_key", right = "x_key")),
factors = list(
S = c("left", "right"),
COLOR = c("red", "blue"),
NOISE = c("high", "moderate", "low")
),
constants = c(st0 = 0, sd_v.false = 1),
accumulators = c("z_key", "x_key"),
type = "lba"
)