make_random_effects: Generate Subject-Level Parameters

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Generate Subject-Level Parameters

Description

Simulates subject-level parameters in the format required by make_data().

Usage

make_random_effects(
  design,
  group_means,
  n_subj = NULL,
  variance_proportion = 0.2,
  covariances = NULL
)

Arguments

design	A design list. The design as specified by design()
group_means	A numeric vector. The group level means for each parameter, in the same order as sampled_pars(design)
n_subj	An integer. The number of subjects to generate parameters for. If NULL will be inferred from design
variance_proportion	A double. Optional. If covariances are not specified, the variances will be created by multiplying the means by this number. The covariances will be 0.
covariances	A covariance matrix. Optional. Specify the intended covariance matrix.

Value

A matrix of subject-level parameters.

Examples

# First create 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 create a group-level means vector:
group_means =c(v_Sleft=-2,v_Sright=2,a=log(1),a_Eneutral=log(1.5),a_Eaccuracy=log(2),
               t0=log(.2),Z=qnorm(.5),sv=log(.5),SZ=qnorm(.5))
# Now we can create subject-level parameters
subj_pars <- make_random_effects(design_DDMaE, group_means, n_subj = 19)

# We can also define a covariance matrix to simulate from
subj_pars <- make_random_effects(design_DDMaE, group_means, n_subj = 19,
             covariances = diag(.1, length(group_means)))

# The subject level parameters can be used to generate data
make_data(subj_pars, design_DDMaE, n_trials = 10)

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