Estimated mean square error of prediction.

ols_fpe(model)

## Arguments

model An object of class lm.

## Value

Final prediction error of the model.

## Details

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

## References

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)