| Summarise | R Documentation |
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.
Summarise(condition, results, fixed_objects)
condition |
a single row from the |
results |
a |
fixed_objects |
object passed down from |
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
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.
\Sexpr[results=rd]{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.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")}
bias, RMSE, RE, EDR,
ECR, MAE, SimExtract
## Not run:
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
}
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.