[R] Obtaining fitted model information
Prof Brian Ripley
ripley at stats.ox.ac.uk
Sun Oct 31 20:34:16 CET 2004
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$rank
> [1] 2
> >
> > # loglik
> > logLik(lm.D9)
> `log Lik.' -20.08824 (df=3)
> >
> > # number of parameters in the model
> > attr(logLik(lm.D9), "df")
> [1] 3
> >
> > # AIC
> > AIC(lm.D9)
> [1] 46.17648
> >
> > c(- 2 * logLik(lm.D9) + 2 * attr(logLik(lm.D9), "df"))
> [1] 46.17648
> >
> > # AICc = AIC + 2 * k * (k + 1)/(n - k - 1)
> >
> > AICc_lm <- function(x){
> + n <- length(resid(x))
> + k <- attr(logLik(lm.D9), "df")
> + AIC(x) + 2 * k * (k + 1) / (n - k - 1)
> + }
> >
> > AICc_lm(lm.D9)
> [1] 47.67648
>
> Best regards,
>
> Renaud
>
>
> John Fox a Ã©crit :
>
> > Dear Thomas,
> >
> > To get the number of independent parameters in the lm object mod, you can
> > use mod$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
> >>-----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
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