Bayesian information criterion for model selection.

## Usage

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

## Arguments

model

An object of class lm.

method

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

## Value

The bayesian information criterion of the model.

## Details

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.

## References

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()

## Examples

# using R computation method
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_sbc(model)
#> [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