| ols_sbic {olsrr} | R Documentation |
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 |
full_model |
An object of class |
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)