design: Specify a Design and Model

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Specify a Design and Model

Description

This function combines information regarding the data, type of model, and the model specification.

Usage

design(
  formula = NULL,
  factors = NULL,
  Rlevels = NULL,
  model,
  data = NULL,
  contrasts = NULL,
  matchfun = NULL,
  constants = NULL,
  covariates = NULL,
  functions = NULL,
  report_p_vector = TRUE,
  custom_p_vector = NULL,
  transform = NULL,
  bound = NULL,
  ...
)

Arguments

formula	A list. Contains the design formulae in the format list(y ~ x, a ~ z).
factors	A named list containing all the factor variables that span the design cells and that should be taken into account by the model. The name subjects must be used to indicate the participant factor variable, also in the data. Example: list(subjects=levels(dat$subjects), condition=levels(dat$condition))
Rlevels	A character vector. Contains the response factor levels. Example: c("right", "left")
model	A function, specifies the model type. Choose from the drift diffusion model (DDM(), DDMt0natural()), the log-normal race model (LNR()), the linear ballistic model (LBA()), the racing diffusion model (RDM(), RDMt0natural()), or define your own model functions.
data	A data frame. data can be used to automatically detect factors, Rlevels and covariates in a dataset. The variable R needs to be a factor variable indicating the response variable. Any numeric column except trials and rt are treated as covariates, and all remaining factor variables are internally used in factors.
contrasts	Optional. A named list specifying a design matrix. Example for supplying a customized design matrix: ⁠list(lM = matrix(c(-1/2,1/2),ncol=1,dimnames=list(NULL,"diff"))))⁠
matchfun	A function. Only needed for race models. Specifies whether a response was correct or not. Example: function(d)d$S==d$lR where lR refers to the latent response factor.
constants	A named vector that sets constants. Any parameter in sampled_pars can be set constant.
covariates	Names of numeric covariates.
functions	List of functions to create new factors based on those in the factors argument. These new factors can then be used in formula.
report_p_vector	Boolean. If TRUE (default), it returns the vector of parameters to be estimated.
custom_p_vector	A character vector. If specified, a custom likelihood function can be supplied.
transform	A list with custom transformations to be applied to the parameters of the model, if the conventional transformations aren't desired. See DDM() for an example of such transformations
bound	A list with custom bounds to be applied to the parameters of the model, if the conventional bound aren't desired. see DDM() for an example of such bounds. Bounds are used to set limits to the likelihood landscape that cannot reasonable be achieved with transform
...	Additional, optional arguments

Value

A design list.

Examples

# load example dataset
dat <- forstmann

# create a function that takes the latent response (lR) factor (d) and returns a logical
# defining the correct response for each stimulus. Here the match is simply
# such that the S factor equals the latent response factor
matchfun <- function(d)d$S==d$lR

# When working with lM and lR, it can be useful to design  an
# "average and difference" contrast matrix. For binary responses, it has a
# simple canonical form
ADmat <- matrix(c(-1/2,1/2),ncol=1,dimnames=list(NULL,"diff"))

# Create a design for a linear ballistic accumulator model (LBA) that allows
# thresholds to be a function of E and lR. The final result is a 9 parameter model.
design_LBABE <- design(data = dat,model=LBA,matchfun=matchfun,
                            formula=list(v~lM,sv~lM,B~E+lR,A~1,t0~1),
                            contrasts=list(v=list(lM=ADmat)),
                            constants=c(sv=log(1)))

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