Compute the relative difference

Source: R/summary_functions.R

Computes the relative difference statistic of the form (est - pop)/ pop, which is equivalent to the form est/pop - 1. If matrices are supplied then an equivalent matrix variant will be used of the form (est - pop) * solve(pop). Values closer to 0 indicate better relative parameter recovery. Note that for single variable inputs this is equivalent to bias(..., type = 'relative').

RD(est, pop, as.vector = TRUE, unname = FALSE)

Arguments

est

a numeric vector, matrix/data.frame, or list containing the parameter estimates

pop

a numeric vector or matrix containing the true parameter values. Must be of comparable dimension to est

as.vector

logical; always wrap the result in a as.vector function before returning?

unname

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

Value

returns a vector or matrix depending on the inputs and whether as.vector was used

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


# vector
pop <- seq(1, 100, length.out=9)
est1 <- pop + rnorm(9, 0, .2)
(rds <- RD(est1, pop))
#> [1]  9.161195e-02 -2.856299e-03 -4.743365e-03 -5.027428e-05  7.186801e-04
#> [6]  8.588291e-05  3.142036e-03  2.113068e-03  1.682020e-03
summary(rds)
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -4.743e-03 -5.027e-05  7.187e-04  1.019e-02  2.113e-03  9.161e-02 

# matrix
pop <- matrix(c(1:8, 10), 3, 3)
est2 <- pop + rnorm(9, 0, .2)
RD(est2, pop, as.vector = FALSE)
#>             [,1]       [,2]        [,3]
#> [1,] -0.09783211  0.6298721 -0.43147808
#> [2,] -0.32422225  0.3607876 -0.07267172
#> [3,]  0.04717509 -0.2042294  0.12395488
(rds <- RD(est2, pop))
#> [1] -0.09783211 -0.32422225  0.04717509  0.62987208  0.36078763 -0.20422939
#> [7] -0.43147808 -0.07267172  0.12395488
summary(rds)
#>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
#> -0.431478 -0.204229 -0.072672  0.003484  0.123955  0.629872