ols_hsp: Hocking's Sp

In olsrr: Tools for Building OLS Regression Models

Hocking's Sp

Description

Average prediction mean squared error.

Usage

ols_hsp(model)

Arguments

model

An object of class lm.

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

Value

Hocking's Sp of the model.

References

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

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)

olsrr documentation built on April 4, 2025, 4:14 a.m.