Bayesian information criterion for model selection.

ols_sbc(model, method = c("R", "STATA", "SAS"))

model | An object of class |
---|---|

method | A character vector; specify the method to compute BIC. Valid options include R, STATA and SAS. |

The bayesian information criterion of the model.

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.

Other model selection criteria:
`ols_aic()`

,
`ols_apc()`

,
`ols_fpe()`

,
`ols_hsp()`

,
`ols_mallows_cp()`

,
`ols_msep()`

,
`ols_sbic()`

#> [1] 167.864# using STATA computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_sbc(model, method = 'STATA')#> [1] 164.3983# using SAS computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_sbc(model, method = 'SAS')#> [1] 73.58622