ols_msep: MSEP

In olsrr: Tools for Building OLS Regression Models

MSEP

Description

Estimated error of prediction, assuming multivariate normality.

Usage

ols_msep(model)

Arguments

model

An object of class lm.

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

Value

Estimated error of prediction of the model.

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)

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