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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