make_data: Simulate Data

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Simulate Data

Description

Simulates data based on a model design and a parameter vector (p_vector) by one of two methods:

1. Creating a fully crossed and balanced design specified by the design, with number of trials per cell specified by the n_trials argument

2. Using the design of a data frame supplied, which allows creation of unbalanced and other irregular designs, and replacing previous data with simulated data

Usage

make_data(
  parameters,
  design = NULL,
  n_trials = NULL,
  data = NULL,
  expand = 1,
  mapped_p = FALSE,
  hyper = FALSE,
  ...
)

Arguments

parameters	parameter vector used to simulate data. Can also be a matrix with one row per subject (with corresponding row names) or an emc object with sampled parameters (in which case posterior medians of alpha are used to simulate data)
design	Design list created by design()
n_trials	Integer. If data is not supplied, number of trials to create per design cell
data	Data frame. If supplied, the factors are taken from the data. Determines the number of trials per level of the design factors and can thus allow for unbalanced designs
expand	Integer. Replicates the data (if supplied) expand times to increase number of trials per cell.
mapped_p	If TRUE instead returns a data frame with one row per design cell and columns for each parameter specifying how they are mapped to the design cells.
hyper	If TRUE the supplied parameters must be a set of samples, from which the group-level will be used to generate subject level parameters. See also make_random_effects to generate subject-level parameters from a hyper distribution.
...	Additional optional arguments

Details

To create data for multiple subjects see ?make_random_effects().

Value

A data frame with simulated data

Examples

# First create a design
design_DDMaE <- design(factors = list(S = c("left", "right"),
                                           E = c("SPD", "ACC"),
                                           subjects = 1:30),
                            Rlevels = c("left", "right"), 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 p_vector:
parameters <- c(v_Sleft=-2,v_Sright=2,a=log(1),a_EACC=log(2), t0=log(.2),
              Z=qnorm(.5),sv=log(.5),SZ=qnorm(.5))

# Now we can simulate data
data <- make_data(parameters, design_DDMaE, n_trials = 30)

# We can also simulate data based on a specific dataset
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)))
parameters <- 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))

data <- make_data(parameters, design_DDMaE, data = forstmann)

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