Summarise simulated data using various population comparison statistics

This collapses the simulation results within each condition to composite estimates such as RMSE, bias, Type I error rates, coverage rates, etc. See the See Also section below for useful functions to be used within Summarise.

Usage

Summarise(condition, results, fixed_objects)

Arguments

condition: a single row from the design input from runSimulation (as a data.frame), indicating the simulation conditions
results: a tibble data frame (if Analyse returned a named numeric vector of any length) or a list (if Analyse returned a list or multi-rowed data.frame) containing the analysis results from Analyse, where each cell is stored in a unique row/list element
fixed_objects: object passed down from runSimulation

Value

for best results should return a named numeric vector or data.frame with the desired meta-simulation results. Named list objects can also be returned, however the subsequent results must be extracted via SimExtract

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. 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. doi:10.1080/10691898.2016.1246953

Examples

if (FALSE) { # \dontrun{

summarise <- function(condition, results, fixed_objects) {

    #find results of interest here (alpha < .1, .05, .01)
    lessthan.05 <- EDR(results, alpha = .05)

    # return the results that will be appended to the design input
    ret <- c(lessthan.05=lessthan.05)
    ret
}

} # }