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.

## 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)

#> [1] 7.949661