Compute the relative performance behavior of collections of standard errors

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 numeric scalar/vector indicating the average standard errors across the replications, or a matrix of collected standard error estimates themselves to be used to compute the average standard errors. Each column/element in this input corresponds to the column/element in SD

SD

a numeric scalar/vector indicating the standard deviation across the replications, or a matrix of collected parameter estimates themselves to be used to compute the standard deviations. Each column/element in this input corresponds to the column/element in SE

percent

logical; change returned result to percentage by multiplying by 100? Default is FALSE

unname

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

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)

Details

Mean-square relative standard error (MSRSE) is expressed as

$$MSRSE = \frac{E(SE(\psi)^2)}{SD(\psi)^2} = \frac{1/R * \sum_{r=1}^R SE(\psi_r)^2}{SD(\psi)^2}$$

where \(SE(\psi_r)\) represents the estimate of the standard error at the \(r\)th simulation replication, and \(SD(\psi)\) represents the standard deviation estimate of the parameters across all \(R\) replications. Note that \(SD(\psi)^2\) is used, which corresponds to the variance of \(\psi\).

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

Author

Phil Chalmers rphilip.chalmers@gmail.com

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)
#> 
#> 
Design: 1/1;   RAM Used: 128.6 Mb;   Replications: 500;   Total Time: 0.00s 
#>  Conditions: dummy_run=NA
#> 

#> 
#> Simulation complete. Total execution time: 0.50s
results
#> # A tibble: 1 × 7
#>   `SE.(Intercept)`   SE.X REPLICATIONS SIM_TIME RAM_USED      SEED COMPLETED    
#>              <dbl>  <dbl>        <dbl> <chr>    <chr>        <int> <chr>        
#> 1          0.98822 1.1781          500 0.50s    129.7 Mb 402147477 Fri Aug 16 1…