Estimated mean square error of prediction.
ols_fpe(model)
| model | An object of class |
|---|
Final prediction error of the model.
Computes the estimated mean square error of prediction for each model selected assuming that the values of the regressors are fixed and that the model is correct.
$$MSE((n + p) / n)$$
where \(MSE = SSE / (n - p)\), n is the sample size and p is the number of predictors including the intercept
Akaike, H. (1969). “Fitting Autoregressive Models for Prediction.” Annals of the Institute of Statistical Mathematics 21:243–247.
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_apc, ols_hsp,
ols_mallows_cp, ols_msep,
ols_sbc, ols_sbic
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_fpe(model)#> [1] 7.949661