MSRSE {SimDesign} | R Documentation |
Compute the relative performance behavior of collections of standard errors
Description
The mean-square relative standard error (MSRSE) compares standard error estimates to the standard deviation of the respective parameter estimates. Values close to 1 indicate that the behavior of the standard errors closely matched the sampling variability of the parameter estimates.
Usage
MSRSE(SE, SD, percent = FALSE, unname = FALSE)
Arguments
SE |
a |
SD |
a |
percent |
logical; change returned result to percentage by multiplying by 100? Default is FALSE |
unname |
logical; apply |
Details
Mean-square relative standard error (MSRSE) is expressed as
where represents the estimate of the standard error at the
th
simulation replication, and
represents the standard deviation estimate
of the parameters across all
replications. Note that
is used,
which corresponds to the variance of
.
Value
returns a vector
of ratios indicating the relative performance
of the standard error estimates to the observed parameter standard deviation.
Values less than 1 indicate that the standard errors were larger than the standard
deviation of the parameters (hence, the SEs are interpreted as more conservative),
while values greater than 1 were smaller than the standard deviation of the
parameters (i.e., more liberal SEs)
Author(s)
Phil Chalmers rphilip.chalmers@gmail.com
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
Generate <- function(condition, fixed_objects) {
X <- rep(0:1, each = 50)
y <- 10 + 5 * X + rnorm(100, 0, .2)
data.frame(y, X)
}
Analyse <- function(condition, dat, fixed_objects) {
mod <- lm(y ~ X, dat)
so <- summary(mod)
ret <- c(SE = so$coefficients[,"Std. Error"],
est = so$coefficients[,"Estimate"])
ret
}
Summarise <- function(condition, results, fixed_objects) {
MSRSE(SE = results[,1:2], SD = results[,3:4])
}
results <- runSimulation(replications=500, generate=Generate,
analyse=Analyse, summarise=Summarise)
results