accuracyOverall {petersenlab}

Overall Accuracy.

Description

Find overall accuracy.

Usage

accuracyOverall(predicted, actual, dropUndefined = FALSE)

Arguments

predicted

vector of continuous predicted values.

actual

vector of actual values.

dropUndefined

TRUE or FALSE, indicating whether to drop any undefined values calculated with the accuracy indices.

Details

Compute overall accuracy estimates of predicted values in relation to actual values. Estimates of overall accuracy span all cutoffs. Some accuracy estimates can be undefined under various circumstances. Optionally, you can drop undefined values in the calculation of accuracy indices. Note that dropping undefined values changes the meaning of these indices. Use this option at your own risk!

Value

• ME = mean error

• MAE = mean absolute error

• MSE = mean squared error

• RMSE = root mean squared error

• MPE = mean percentage error

• MAPE = mean absolute percentage error

• sMAPE = symmetric mean absolute percentage error

• MASE = mean absolute scaled error

• RMSLE = root mean squared log error

• rsquared = R-squared

• rsquaredAdj = adjusted R-squared

• rsquaredPredictive = predictive R-squared

See Also

Mean absolute scaled error (MASE):
https://stats.stackexchange.com/questions/108734/alternative-to-mape-when-the-data-is-not-a-time-series
https://stats.stackexchange.com/questions/322276/is-mase-specified-only-to-time-series-data
https://stackoverflow.com/questions/31197726/calculate-mase-with-cross-sectional-non-time-series-data-in-r
https://stats.stackexchange.com/questions/401759/how-can-mase-mean-absolute-scaled-error-score-value-be-interpreted-for-non-tim

Predictive R-squared:
https://www.r-bloggers.com/2014/05/can-we-do-better-than-r-squared/

Other accuracy: accuracyAtCutoff(), accuracyAtEachCutoff(), nomogrammer(), optimalCutoff()

Examples

# Prepare Data
data("USArrests")

# Calculate Accuracy
accuracyOverall(predicted = USArrests$Assault, actual = USArrests$Murder)