| ols_fpe {olsrr} | R Documentation |
Final prediction error
Description
Estimated mean square error of prediction.
Usage
ols_fpe(model)
Arguments
model |
An object of class |
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
Value
Final prediction error of the model.
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.
See Also
Other model selection criteria:
ols_aic(),
ols_apc(),
ols_hsp(),
ols_mallows_cp(),
ols_msep(),
ols_sbc(),
ols_sbic()
Examples
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_fpe(model)