simulate.model: Simulate response time data
In ggdmc: Cognitive Models

Description Usage Arguments Details Value

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.

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## S3 method for class 'model'
simulate(object, nsim = NA, seed = NULL, nsub = NA,
  prior = NA, ps = NA, ...)

`object`	a model object.
`nsim`	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.
`seed`	a user specified random seed.
`nsub`	number of subjects
`prior`	a prior object
`ps`	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.

a data frame

ggdmc documentation built on May 2, 2019, 9:59 a.m.