| ols_apc {olsrr} | R Documentation |
Amemiya's prediction criterion
Description
Amemiya's prediction error.
Usage
ols_apc(model)
Arguments
model |
An object of class |
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)