# ols_aic: Akaike information criterion In cmlopera/olsrr: Tools for Building OLS Regression Models

## Description

Akaike information criterion for model selection.

## Usage

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

## Arguments

 `model` An object of class `lm`. `method` A character vector; specify the method to compute AIC. Valid options include R, STATA and SAS.

## Details

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.

## Value

Akaike information criterion of the model.

## References

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`
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# using R computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_aic(model) # using STATA computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_aic(model, method = 'STATA') # using SAS computation method model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_aic(model, method = 'SAS') ```