SimDesign: Summarise – R documentation

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Summarise

Summarise simulated data using various population comparison statistics

Description

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 = NULL)

Arguments

`condition`	a single row from the `design` input from `runSimulation` (as a `data.frame`), indicating the simulation conditions
`results`	a `data.frame` (if `Analyse` returned a named numeric vector of any length), a vector (if `Analyse` returned a single unnamed numeric value), 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

must return a named numeric vector or data.frame with the desired meta-simulation results

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

## Not run: 

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

    #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
}


## End(Not run)

SimDesign

Structure for Organizing Monte Carlo Simulation Designs

v2.3

GPL (>= 2)

Authors

Phil Chalmers [aut, cre] (<https://orcid.org/0000-0001-5332-2810>), Matthew Sigal [ctb], Ogreden Oguzhan [ctb]

Initial release