Sawa's bayesian information criterion for model selection.
ols_sbic(model, full_model)
model | An object of class |
---|---|
full_model | An object of class |
Sawa's Bayesian Information Criterion
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.
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.
Other model selection criteria: ols_aic
,
ols_apc
, ols_fpe
,
ols_hsp
, ols_mallows_cp
,
ols_msep
, ols_sbc
full_model <- lm(mpg ~ ., data = mtcars) model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_sbic(model, full_model)#> Error in subtract(., b): could not find function "subtract"