Efron's pseudo r-squared

Produces Efron's pseudo r-squared from certain models, or vectors of residuals, predicted values, and actual values. Alternately produces minimum maximum accuracy, mean absolute percent error, root mean square error, or coefficient of variation.

Usage

efronRSquared(
  model = NULL,
  actual = NULL,
  predicted = NULL,
  residual = NULL,
  statistic = "EfronRSquared",
  plotit = FALSE,
  digits = 3,
  ...
)

Arguments

model

A model of the class lm, glm, nls, betareg, gls, lme, lmerMod, lmerModLmerTest, glmmTMB, rq, loess, gam, negbin, glmRob, rlm, or mblm.

actual

A vector of actual y values

predicted

A vector of predicted values

residual

A vector of residuals

statistic

The statistic to produce. One of "EfronRSquared", "MinMaxAccuracy", "MAE", "MAPE", "MSE", "RMSE", "NRMSE.Mean", "CV".

plotit

If TRUE, produces plots of the predicted values vs. the actual values.

digits

The number of significant digits in the output.

...

Other arguments passed to plot.

Value

A single statistic

Details

Efron's pseudo r-squared is calculated as 1 minus the residual sum of squares divided by the total sum of squares. For linear models (lm model objects), Efron's pseudo r-squared will be equal to r-squared.

This function produces the same statistics as does the accuracy function. While the accuracy function extracts values from a model object, this function allows for the manual entry of residual, predicted, or actual values.

It is recommended that the user consults the accuracy function for further details on these statistics, such as if the reported value is presented as a percentage or fraction.

If modelis not supplied, two of the following need to passed to the function: actual, predicted, residual.

Note that, for some model objects, to extract residuals and predicted values on the original scale, a type="response" option needs to be added to the call, e.g. residuals(model.object, type="response").

References

https://rcompanion.org/handbook/F_16.html

See also

accuracy, nagelkerke

Author

Salvatore Mangiafico, mangiafico@njaes.rutgers.edu

Examples

data(BrendonSmall)
BrendonSmall$Calories = as.numeric(BrendonSmall$Calories)
BrendonSmall$Calories2 = BrendonSmall$Calories ^ 2
model.1 = lm(Sodium ~ Calories + Calories2, data = BrendonSmall)

efronRSquared(model.1)
#> EfronRSquared 
#>         0.862 

efronRSquared(model.1, statistic="MAPE")
#>  MAPE 
#> 0.017 

efronRSquared(actual=BrendonSmall$Sodium, residual=model.1$residuals)
#> EfronRSquared 
#>         0.862 
efronRSquared(residual=model.1$residuals, predicted=model.1$fitted.values)
#> EfronRSquared 
#>         0.862 
efronRSquared(actual=BrendonSmall$Sodium, predicted=model.1$fitted.values)
#> EfronRSquared 
#>         0.862 

summary(model.1)$r.squared
#> [1] 0.8621318