Summarise: Summarise simulated data using various population comparison...
In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

Description Usage Arguments Value See Also Examples

View source: R/functions.R

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.

1	Summarise(condition, results, fixed_objects = NULL)

`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 numeric vector) 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`

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

bias, RMSE, RE, EDR, ECR, MAE

## Not run: 

mysummarise <- 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)