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)#> [1] 70.27599