Akaike information criterion for model selection.

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

## Arguments

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

## Value

Akaike information criterion of the model.

## 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.

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.

## See also

Other model selection criteria: ols_apc(), ols_fpe(), ols_hsp(), ols_mallows_cp(), ols_msep(), ols_sbc(), ols_sbic()

## Examples

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