SimDesign (version 2.17.1)

Generate: Generate data

Description

Generate data from a single row in the design input (see runSimulation). R contains numerous approaches to generate data, some of which are contained in the base package, as well as in SimDesign (e.g., rmgh, rValeMaurelli, rHeadrick). However the majority can be found in external packages. See CRAN's list of possible distributions here: https://CRAN.R-project.org/view=Distributions. Note that this function technically can be omitted if the data generation is provided in the Analyse step, though in general this is not recommended.

Usage

Generate(condition, fixed_objects)

Value

returns a single object containing the data to be analyzed (usually a

vector, matrix, or data.frame), or list

Arguments

condition

a single row from the design input (as a data.frame), indicating the simulation conditions

fixed_objects

object passed down from runSimulation

Details

The use of try functions is generally not required in this function because Generate is internally wrapped in a try call. Therefore, if a function stops early then this will cause the function to halt internally, the message which triggered the stop will be recorded, and Generate will be called again to obtain a different dataset. That said, it may be useful for users to throw their own stop commands if the data should be re-drawn for other reasons (e.g., an estimated model terminated correctly but the maximum number of iterations were reached).

References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248")

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")

See Also

addMissing, Attach, rmgh, rValeMaurelli, rHeadrick

Examples

if (FALSE) {

generate <- function(condition, fixed_objects) {
    N1 <- condition$sample_sizes_group1
    N2 <- condition$sample_sizes_group2
    sd <- condition$standard_deviations

    group1 <- rnorm(N1)
    group2 <- rnorm(N2, sd=sd)
    dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)),
                      DV = c(group1, group2))
    # just a silly example of a simulated parameter
    pars <- list(random_number = rnorm(1))

    list(dat=dat, parameters=pars)
}

# similar to above, but using the Attach() function instead of indexing
generate <- function(condition, fixed_objects) {
    Attach(condition)
    N1 <- sample_sizes_group1
    N2 <- sample_sizes_group2
    sd <- standard_deviations

    group1 <- rnorm(N1)
    group2 <- rnorm(N2, sd=sd)
    dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)),
                      DV = c(group1, group2))
    dat
}

generate2 <- function(condition, fixed_objects) {
    mu <- sample(c(-1,0,1), 1)
    dat <- rnorm(100, mu)
    dat        #return simple vector (discard mu information)
}

generate3 <- function(condition, fixed_objects) {
    mu <- sample(c(-1,0,1), 1)
    dat <- data.frame(DV = rnorm(100, mu))
    dat
}

}