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