Average prediction mean squared error.

ols_hsp(model)

model | An object of class |
---|

Hocking's Sp of the model.

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

Hocking, R. R. (1976). “The Analysis and Selection of Variables in a Linear Regression.” Biometrics 32:1–50.

Other model selection criteria: `ols_aic`

,
`ols_apc`

, `ols_fpe`

,
`ols_mallows_cp`

, `ols_msep`

,
`ols_sbc`

, `ols_sbic`

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_hsp(model)#> [1] 0.2644378