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Root Mean Squared Error

Source: R/performance_rmse.R
performance_rmse.Rd

Compute root mean squared error for (mixed effects) models, including Bayesian regression models.

Usage

performance_rmse(
  model,
  normalized = FALSE,
  ci = NULL,
  iterations = 100,
  ci_method = NULL,
  verbose = TRUE,
  ...
)

rmse(
  model,
  normalized = FALSE,
  ci = NULL,
  iterations = 100,
  ci_method = NULL,
  verbose = TRUE,
  ...
)

Arguments

model

A model.

normalized

Logical, use TRUE if normalized rmse should be returned.

ci

Confidence resp. credible interval level. For icc(), r2(), and rmse(), confidence intervals are based on bootstrapped samples from the ICC, R2 or RMSE value. See iterations.

iterations

Number of bootstrap-replicates when computing confidence intervals for the ICC, R2, RMSE etc.

ci_method

Character string, indicating the bootstrap-method. Should be NULL (default), in which case lme4::bootMer() is used for bootstrapped confidence intervals. However, if bootstrapped intervals cannot be calculated this way, try ci_method = "boot", which falls back to boot::boot(). This may successfully return bootstrapped confidence intervals, but bootstrapped samples may not be appropriate for the multilevel structure of the model. There is also an option ci_method = "analytical", which tries to calculate analytical confidence assuming a chi-squared distribution. However, these intervals are rather inaccurate and often too narrow. It is recommended to calculate bootstrapped confidence intervals for mixed models.

verbose

Toggle warnings and messages.

...

Arguments passed down to lme4::bootMer() or boot::boot() for bootstrapped ICC, R2, RMSE etc.; for variance_decomposition(), arguments are passed down to brms::posterior_predict().

Value

Numeric, the root mean squared error.

Details

The RMSE is the square root of the variance of the residuals and indicates the absolute fit of the model to the data (difference between observed data to model's predicted values). It can be interpreted as the standard deviation of the unexplained variance, and is in the same units as the response variable. Lower values indicate better model fit.

The normalized RMSE is the proportion of the RMSE related to the range of the response variable. Hence, lower values indicate less residual variance.

Examples

data(Orthodont, package = "nlme")
m <- nlme::lme(distance ~ age, data = Orthodont)

# RMSE
performance_rmse(m, normalized = FALSE)
#> [1] 1.086327

# normalized RMSE
performance_rmse(m, normalized = TRUE)
#> [1] 0.07242178