ggdmcModel (version 0.2.9.0)

BuildModel: 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
)

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.

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.

• head prints the first few elements.

• sample samples and prints a handful of elements.

• all prints all elements.

. Default to head method.

verbose

Logical; if TRUE, prints design information.

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"
)