rmserr: Accuracy Measures

View source: R/rmserr.R

rmserrR Documentation

Accuracy Measures

Description

Calculates different accuracy measures, most prominently RMSE.

Usage

rmserr(x, y, summary = FALSE)

Arguments

x, y

two vectors of real numbers

summary

logical; should a summary be printed to the screen?

Details

Calculates six different measures of accuracy for two given vectors or sequences of real numbers:

MAE Mean Absolute Error
MSE Mean Squared Error
RMSE Root Mean Squared Error
MAPE Mean Absolute Percentage Error
LMSE Normalized Mean Squared Error
rSTD relative Standard Deviation

Value

Returns a list with different accuracy measures.

Note

Often used in Data Mining for predicting the accuracy of predictions.

References

Gentle, J. E. (2009). Computational Statistics, section 10.3. Springer Science+Business Media LCC, New York.

Examples

x <- rep(1, 10)
y <- rnorm(10, 1, 0.1)
rmserr(x, y, summary = TRUE)

pracma documentation built on Nov. 10, 2023, 1:14 a.m.