Average prediction mean squared error.

ols_hsp(model)

An object of class lm.

lm

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). <U+201C>The Analysis and Selection of Variables in a Linear Regression.<U+201D> Biometrics 32:1<U+2013>50.

Other model selection criteria: ols_aic, ols_apc, ols_fpe, ols_mallows_cp, ols_msep, ols_sbc, ols_sbic

ols_aic

ols_apc

ols_fpe

ols_mallows_cp

ols_msep

ols_sbc

ols_sbic

# NOT RUN { model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_hsp(model) # }

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