Akaike information criterion for model selection.

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

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

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

Akaike information criterion of the model.

AIC provides a means for model selection. Given a collection of models for
the data, AIC estimates the quality of each model, relative to each of the
other models. R and STATA use loglikelihood to compute AIC. SAS uses residual
sum of squares. Below is the formula in each case:
*R & STATA*
$$AIC = -2(loglikelihood) + 2p$$
*SAS*
$$AIC = n * ln(SSE / n) + 2p$$

where *n* is the sample size and *p* is the number of model parameters including intercept.

Akaike, H. (1969). “Fitting Autoregressive Models for Prediction.” Annals of the Institute of Statistical Mathematics 21:243–247.

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_apc`

,
`ols_fpe`

, `ols_hsp`

,
`ols_mallows_cp`

, `ols_msep`

,
`ols_sbc`

, `ols_sbic`

# using R computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_aic(model)#> [1] 159.0696# using STATA computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_aic(model, method = 'STATA')#> [1] 157.0696# using SAS computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_aic(model, method = 'SAS')#> [1] 66.25754