[R] Obtaining fitted model information

[Prof Brian Ripley]

The harder task is actually to get `n', not `npar'.

length(resid(x)) may or may not include missing values, depending on the 
na.action used, and will include observations with weight zero.
However, logLik's "lm" method returns an attribute "nobs" that is a better 
choice.

On Sun, 31 Oct 2004, Renaud Lancelot wrote:

> 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$rank
> [1] 2
>  >
>  > # loglik
>  > logLik(lm.D9)
> `log Lik.' -20.08824 (df=3)
>  >
>  > # number of parameters in the model
>  > attr(logLik(lm.D9), "df")
> [1] 3
>  >
>  > # AIC
>  > AIC(lm.D9)
> [1] 46.17648
>  >
>  > c(- 2 * logLik(lm.D9) + 2 * attr(logLik(lm.D9), "df"))
> [1] 46.17648
>  >
>  > # AICc = AIC + 2 * k * (k + 1)/(n - k - 1)
>  >
>  > AICc_lm <- function(x){
> +   n <- length(resid(x))
> +   k <- attr(logLik(lm.D9), "df")
> +   AIC(x) + 2 * k * (k + 1) / (n - k - 1)
> +   }
>  >
>  > AICc_lm(lm.D9)
> [1] 47.67648
> 
> Best regards,
> 
> Renaud
> 
> 
> John Fox a écrit :
> 
> > Dear Thomas,
> > 
> > To get the number of independent parameters in the lm object mod, you can
> > use mod$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
> >>-----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

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595