| ols_aic {olsrr} | R Documentation |
Akaike information criterion
Description
Akaike information criterion for model selection.
Usage
ols_aic(model, method = c("R", "STATA", "SAS"), corrected = FALSE)
Arguments
model |
An object of class |
method |
A character vector; specify the method to compute AIC. Valid options include R, STATA and SAS. |
corrected |
Logical; if |
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
corrected
AIC = n * ln(SSE / n) + ((n * (n + p)) / (n - p - 2))
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.
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)
# 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')
# corrected akaike information criterion
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_aic(model, method = 'SAS', corrected = TRUE)