ols_apc: Amemiya's prediction criterion

In olsrr: Tools for Building OLS Regression Models

Amemiya's prediction criterion

Description

Amemiya's prediction error.

Usage

ols_apc(model)

Arguments

model

An object of class lm.

Details

Amemiya's Prediction Criterion penalizes R-squared more heavily than does adjusted R-squared for each addition degree of freedom used on the right-hand-side of the equation. The lower the better for this criterion.

((n + p) / (n - p))(1 - (R^2))

where n is the sample size, p is the number of predictors including the intercept and R^2 is the coefficient of determination.

Value

Amemiya's prediction error of the model.

References

Amemiya, T. (1976). Selection of Regressors. Technical Report 225, Stanford University, Stanford, CA.

Judge, G. G., Griffiths, W. E., Hill, R. C., and Lee, T.-C. (1980). The Theory and Practice of Econometrics. New York: John Wiley & Sons.

See Also

Other model selection criteria: ols_aic(), ols_fpe(), ols_hsp(), ols_mallows_cp(), ols_msep(), ols_sbc(), ols_sbic()

Examples

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_apc(model)

olsrr documentation built on May 29, 2024, 12:35 p.m.