Description Usage Arguments Details Value References See Also Examples

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')
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.