SimDesign (version 2.18)

RSE: Compute the relative standard error ratio

Description

Computes the relative standard error ratio given the set of estimated standard errors (SE) and the deviation across the R simulation replications (SD). The ratio is formed by finding the expectation of the SE terms, and compares this expectation to the general variability of their respective parameter estimates across the R replications (ratio should equal 1). This is used to roughly evaluate whether the SEs being advertised by a given estimation method matches the sampling variability of the respective estimates across samples.

Usage

RSE(SE, ests, unname = FALSE)

Value

returns vector of variance ratios, (RSV = SE^2/SD^2)

Arguments

SE

a numeric matrix of SE estimates across the replications (extracted from the results object in the Summarise step). Alternatively, can be a vector containing the mean of the SE estimates across the R simulation replications

ests

a numeric matrix object containing the parameter estimates under investigation found within the Summarise function. This input is used to compute the standard deviation/variance estimates for each column to evaluate how well the expected SE matches the standard deviation

unname

logical; apply unname to the results to remove any variable names?

Author

Phil Chalmers

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. 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. tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")

Examples

R <- 10000
par_ests <- cbind(rnorm(R), rnorm(R, sd=1/10),
                  rnorm(R, sd=1/15))
colnames(par_ests) <- paste0("par", 1:3)
(SDs <- colSDs(par_ests))

SEs <- cbind(1 + rnorm(R, sd=.01),
             1/10 + + rnorm(R, sd=.01),
             1/15 + rnorm(R, sd=.01))
(E_SEs <- colMeans(SEs))
RSE(SEs, par_ests)

# equivalent to the form
colMeans(SEs) / SDs