SimDesign: RMSE – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

RMSE

Compute the (normalized) root mean square error

Description

Computes the average deviation (root mean square error; also known as the root mean square 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

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

RMSD(
  estimate,
  parameter = NULL,
  type = "RMSE",
  MSE = FALSE,
  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 `sqrt(mean(estimate^2))` will be returned)
`type`	type of deviation to compute. Can be `'RMSE'` (default) for the root mean square-error, `'NRMSE'` for the normalized RMSE (RMSE / (max(estimate) - min(estimate))), `'SRMSE'` for the standardized RMSE (RMSE / sd(estimate)), `'CV'` for the coefficient of variation, or `'RMSLE'` for the root mean-square log-error
`MSE`	logical; return the mean square error equivalent of the results instead of the root mean-square error (in other words, the result is squared)? Default is `FALSE`
`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 average deviation 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)
RMSE(samp, pop)

dev <- samp - pop
RMSE(dev)

RMSE(samp, pop, type = 'NRMSE')
RMSE(dev, type = 'NRMSE')
RMSE(dev, pop, type = 'SRMSE')
RMSE(samp, pop, type = 'CV')
RMSE(samp, pop, type = 'RMSLE')

# percentage reported
RMSE(samp, pop, type = 'NRMSE')
RMSE(samp, pop, type = 'NRMSE', percent = TRUE)

# matrix input
mat <- cbind(M1=rnorm(100, 2, sd = 0.5), M2 = rnorm(100, 2, sd = 1))
RMSE(mat, parameter = 2)
RMSE(mat, parameter = c(2, 3))

# different parameter associated with each column
mat <- cbind(M1=rnorm(1000, 2, sd = 0.25), M2 = rnorm(1000, 3, sd = .25))
RMSE(mat, parameter = c(2,3))

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

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