Simulate response time data either for one subject or multiple subjects.
The simulation is based on a model object. For one subject, one must supply
a true parameter vector to the ps argument.
# S3 method for model
simulate(object, nsim = NA, seed = NULL, nsub = NA, prior = NA, ps = NA, ...)a data frame
a model object.
number of trials / responses. n can be a single number
for a balanced design or a matrix for an unbalanced design, where rows
are subjects and columns are design cells. If the matrix has one row then
all subjects have the same n in each cell, if it has one column then
all cells have the same n; Otherwise each entry specifies the
n for a particular subject x design cell combination.
a user specified random seed.
number of subjects
a prior object
a true parameter vector or matrix.
additional optional arguments.
For multiple subjects, one can enter a matrix (or a row vector) as true
parameters. Each row is to generate data separately for a subject. This is
the fixed-effect model. To generate data based on a random-effect
model, one must supply a prior object. In this case, ps argument
is unused. Note in some cases, a random-effect model may fail to draw data
from the model, because true parameters are randomly drawn from
a prior object. This would happen sometimes in diffusion model, because
certain parameter combinations are considered invalid.
ps can be a row vector, in which case each subject has identical
parameters. It can also be a matrix with one row per subject, in which
case it must have ns rows. The true values will be saved as
parameters attribute in the output object.