# [R] Obtaining fitted model information

Renaud Lancelot renaud.lancelot at cirad.fr
Sun Oct 31 20:06:57 CET 2004

```With models estimated with lm, the number of parameters is obtained
adding 1 to the rank of the fitted model (to account for the residuals
variance). The number of parameters is found in logLik objects:

> # example from ?lm
> ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
> trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
> group <- gl(2,10,20, labels=c("Ctl","Trt"))
> weight <- c(ctl, trt)
> lm.D9 <- lm(weight ~ group)
>
> # rank of the model
> lm.D9\$rank
[1] 2
>
> # loglik
> logLik(lm.D9)
`log Lik.' -20.08824 (df=3)
>
> # number of parameters in the model
> attr(logLik(lm.D9), "df")
[1] 3
>
> # AIC
> AIC(lm.D9)
[1] 46.17648
>
> c(- 2 * logLik(lm.D9) + 2 * attr(logLik(lm.D9), "df"))
[1] 46.17648
>
> # AICc = AIC + 2 * k * (k + 1)/(n - k - 1)
>
> AICc_lm <- function(x){
+   n <- length(resid(x))
+   k <- attr(logLik(lm.D9), "df")
+   AIC(x) + 2 * k * (k + 1) / (n - k - 1)
+   }
>
> AICc_lm(lm.D9)
[1] 47.67648

Best regards,

Renaud

John Fox a Ã©crit :

> Dear Thomas,
>
> To get the number of independent parameters in the lm object mod, you can
> use mod\$rank, sum(!is.na(coef(mod)), or -- if there are no linear
> dependencies among the columns of the model matrix -- length(coef(mod)).
>
> I hope this helps,
>  John
>
> --------------------------------
> John Fox
> Department of Sociology
> McMaster University
> Hamilton, Ontario
> Canada L8S 4M4
> 905-525-9140x23604
> http://socserv.mcmaster.ca/jfox
> --------------------------------
>
>
>>-----Original Message-----
>>From: r-help-bounces at stat.math.ethz.ch
>>[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Thomas
>>W Volscho
>>Sent: Sunday, October 31, 2004 12:41 PM
>>To: r-help at stat.math.ethz.ch
>>Subject: [R] Obtaining fitted model information
>>
>>Dear list,
>>I am brand new to R  and using Dalgaard's (2002) book
>>Introductory Statistics with R (thus, some of my terminology
>>may be incorrect).
>>
>>I am fitting regression models and I want to use Hurvich and
>>Tsai's AICC statistic to examine my regression models.  This
>>penalty can be expressed as: 2*npar * (n/(n-npar-1)).
>>
>>While you can obtain AIC, BIC, and logLik, I want to impose
>>the AICC penalty instead.
>>
>>After fitting a model.  Is there a way of obtaining the
>>"npar" and then assigning it to a variable?
>>
>>Essentially, I want to then write a simple function to add
>>the AICC penalty to (-2*logLik).
>>
>>Thank you in advance for any help,
>>Tom Volscho
>>
>>************************************
>>Thomas W. Volscho
>>Dept. of Sociology U-2068
>>University of Connecticut
>>Storrs, CT 06269
>>Phone: (860) 486-3882
>>http://vm.uconn.edu/~twv00001
>>
>>______________________________________________
>>R-help at stat.math.ethz.ch mailing list
>>https://stat.ethz.ch/mailman/listinfo/r-help
>>http://www.R-project.org/posting-guide.html
>
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
>

--