Akaike information criterion for model selection.
An object of class
A character vector; specify the method to compute AIC. Valid options include R, STATA and SAS.
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
AIC = n * ln(SSE / n) + 2p
where n is the sample size and p is the number of model parameters including intercept.
Akaike information criterion of the model.
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.
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# 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')
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