Bayesian information criterionSource:
Bayesian information criterion for model selection.
ols_sbc(model, method = c("R", "STATA", "SAS"))
An object of class
A character vector; specify the method to compute BIC. Valid options include R, STATA and SAS.
SBC provides a means for model selection. Given a collection of models for the data, SBC estimates the quality of each model, relative to each of the other models. R and STATA use loglikelihood to compute SBC. SAS uses residual sum of squares. Below is the formula in each case:
R & STATA $$AIC = -2(loglikelihood) + ln(n) * 2p$$
SAS $$AIC = n * ln(SSE / n) + p * ln(n)$$
where n is the sample size and p is the number of model parameters including intercept.
Schwarz, G. (1978). “Estimating the Dimension of a Model.” Annals of Statistics 6:461–464.
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.
# using R computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_sbc(model) #>  167.864 # using STATA computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_sbc(model, method = 'STATA') #>  164.3983 # using SAS computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_sbc(model, method = 'SAS') #>  73.58622