Estimated error of prediction, assuming multivariate normality.

ols_msep(model)

Arguments

model

An object of class lm.

Value

Estimated error of prediction of the model.

Details

Computes the estimated mean square error of prediction assuming that both independent and dependent variables are multivariate normal. $$MSE(n + 1)(n - 2) / n(n - p - 1)$$

where \(MSE = SSE / (n - p)\), n is the sample size and p is the number of predictors including the intercept

References

Stein, C. (1960). “Multiple Regression.” In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, edited by I. Olkin, S. G. Ghurye, W. Hoeffding, W. G. Madow, and H. B. Mann, 264–305. Stanford, CA: Stanford University Press.

Darlington, R. B. (1968). “Multiple Regression in Psychological Research and Practice.” Psychological Bulletin 69:161–182.

See also

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

Examples

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_msep(model)
#> [1] 8.181044