ols_sbic: Sawa's bayesian information criterion

In olsrr: Tools for Building OLS Regression Models

Sawa's bayesian information criterion

Description

Sawa's bayesian information criterion for model selection.

Usage

ols_sbic(model, full_model)

Arguments

model	An object of class lm.
full_model	An object of class lm.

Details

Sawa (1978) developed a model selection criterion that was derived from a Bayesian modification of the AIC criterion. Sawa's Bayesian Information Criterion (BIC) is a function of the number of observations n, the SSE, the pure error variance fitting the full model, and the number of independent variables including the intercept.

SBIC = n * ln(SSE / n) + 2(p + 2)q - 2(q^2)

where q = n(\sigma^2)/SSE, n is the sample size, p is the number of model parameters including intercept SSE is the residual sum of squares.

Value

Sawa's Bayesian Information Criterion

References

Sawa, T. (1978). “Information Criteria for Discriminating among Alternative Regression Models.” Econometrica 46:1273–1282.

Judge, G. G., Griffiths, W. E., Hill, R. C., and Lee, T.-C. (1980). The Theory and Practice of Econometrics. New York: John Wiley & Sons.

See Also

Other model selection criteria: ols_aic(), ols_apc(), ols_fpe(), ols_hsp(), ols_mallows_cp(), ols_msep(), ols_sbc()

Examples

full_model <- lm(mpg ~ ., data = mtcars)
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_sbic(model, full_model)

olsrr documentation built on May 29, 2024, 12:35 p.m.