Description Usage Arguments Details Value References See Also Examples
View source: R/ols-information-criteria.R
Akaike information criterion for model selection.
1 |
model |
An object of class |
method |
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
SAS
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.
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')
|
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