simER function

Simulates sequential testing with evidence ratios

Simulates one or many sequential testing with evidence ratios from independent two-groups comparisons, as a function of sample size and standardized mean difference. Evidence ratios are computed from the so-called Akaike weights from either the Akaike Information Criterion or the Bayesian Information Criterion.

simER(cohensd = 0, nmin = 20, nmax = 100, boundary = 10, nsims = 20,
  ic = bic, cores = 2, verbose = FALSE)

Arguments

• cohensd: Expected effect size
• nmin: Minimum sample size from which start computing ERs
• nmax: Maximum sample size at which stop computing ERs
• boundary: The Evidence Ratio (or its reciprocal) at which the run is stopped as well
• nsims: Number of simulated samples (should be dividable by cores)
• ic: Indicates whether to use the aic or the bic
• cores: Number of parallel processes. If cores is set to 1, no parallel framework is used (default is two cores).
• verbose: Show output about progress

Returns

An object of class data.frame, which contains...

Examples

## Not run:

sim <- simER(cohensd = 0.8, nmin = 20, nmax = 100, boundary = 10,
nsims = 100, ic = bic, cores = 2, verbose = TRUE)
plot(sim, log = TRUE, hist = TRUE)
## End(Not run)

See Also

ictab, analysER

Author(s)

Ladislas Nalborczyk <ladislas.nalborczyk@gmail.com >