Average prediction mean squared error.

ols_hsp(model)

## Arguments

model |
An object of class `lm` . |

## Value

Hocking's Sp of the model.

## Details

Hocking's Sp criterion is an adjustment of the residual sum of
Squares. Minimize this criterion.
$$MSE / (n - p - 1)$$

where \(MSE = SSE / (n - p)\), n is the sample size and p is the number of predictors including the intercept

## References

Hocking, R. R. (1976). The Analysis and Selection of Variables in a Linear Regression. Biometrics
32:150.

## See also

Other model selection criteria: `ols_aic`

,
`ols_apc`

, `ols_fpe`

,
`ols_mallows_cp`

, `ols_msep`

,
`ols_sbc`

, `ols_sbic`

## Examples

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_hsp(model)

#> [1] 0.2644378