ols_fpe: Final prediction error

In cmlopera/olsrr: Tools for Building OLS Regression Models

Description

Estimated mean square error of prediction.

Usage

ols_fpe(model)

Arguments

model

An object of class lm.

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)

cmlopera/olsrr documentation built on May 26, 2019, 10:34 a.m.