SimDesign: MAE – R documentation

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MAE

Compute the mean absolute error

Description

Computes the average absolute deviation of a sample estimate from the parameter value. Accepts estimate and parameter values, as well as estimate values which are in deviation form.

Usage

MAE(estimate, parameter = NULL, type = "MAE", percent = FALSE, unname = FALSE)

Arguments

`estimate`	a `numeric` vector or `matrix`/`data.frame` of parameter estimates. If a vector, the length is equal to the number of replications. If a `matrix`/`data.frame` the number of rows must equal the number of replications
`parameter`	a `numeric` scalar/vector indicating the fixed parameter values. If a single value is supplied and `estimate` is a `matrix`/`data.frame` then the value will be recycled for each column; otherwise, each element will be associated with each respective column in the `estimate` input. If `NULL`, then it will be assumed that the `estimate` input is in a deviation form (therefore `mean(abs(estimate))` will be returned)
`type`	type of deviation to compute. Can be `'MAE'` (default) for the mean absolute error, `'NMSE'` for the normalized MAE (MAE / (max(estimate) - min(estimate))), or `'SMSE'` for the standardized MAE (MAE / sd(estimate))
`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 numeric vector indicating the overall mean absolute error in the estimates

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

pop <- 1
samp <- rnorm(100, 1, sd = 0.5)
MAE(samp, pop)

dev <- samp - pop
MAE(dev)
MAE(samp, pop, type = 'NMAE')
MAE(samp, pop, type = 'SMAE')

# matrix input
mat <- cbind(M1=rnorm(100, 2, sd = 0.5), M2 = rnorm(100, 2, sd = 1))
MAE(mat, parameter = 2)

# same, but with data.frame
df <- data.frame(M1=rnorm(100, 2, sd = 0.5), M2 = rnorm(100, 2, sd = 1))
MAE(df, parameter = c(2,2))

# parameters of the same size
parameters <- 1:10
estimates <- parameters + rnorm(10)
MAE(estimates, parameters)

SimDesign

Structure for Organizing Monte Carlo Simulation Designs

v2.3

GPL (>= 2)

Authors

Phil Chalmers [aut, cre] (<https://orcid.org/0000-0001-5332-2810>), Matthew Sigal [ctb], Ogreden Oguzhan [ctb]

Initial release

MAE