| ols_msep {olsrr} | R Documentation |
MSEP
Description
Estimated error of prediction, assuming multivariate normality.
Usage
ols_msep(model)
Arguments
model |
An object of class |
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)