Amemiya's prediction error.

ols_apc(model)

## Arguments

model An object of class lm.

## Value

Amemiya's prediction error of the model.

## 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 higher 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.

## 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.

Other model selection criteria: ols_aic, ols_fpe, ols_hsp, ols_mallows_cp, ols_msep, ols_sbc, ols_sbic
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)