Re: [R] GAM and AIC: How can I do??? please

From: Martin Maechler <maechler_at_stat.math.ethz.ch>
Date: Mon 24 Oct 2005 - 17:49:00 EST

>>>>> "ronggui" == ronggui <042045003@fudan.edu.cn> >>>>> on Mon, 24 Oct 2005 10:09:30 +0800 writes:

    ronggui> ======= 2005-10-24 09:55:32     ronggui> ˙┌┤┼đ┤└║=======

>> Hello, I'm a Korean researcher who have been started to
>> learn the "R" package.
>>
>> I want to make gam model and AIC value of the model to
>> compare several models.
>>
>> I did the GAM model, but there were error for AIC.
>> SO, how can I do? pleas help me!!!
>>
>>
>> I did like below;

>>
>> > a.fit <- gam(pi~ s(t1r), family = gaussian(link="log"))
>> > summary(a.fit)

>> > Family: gaussian
>> > Link function: log
>> >
>> > Formula:
>> > pi ~ s(t1r)
>> >
>> > Parametric coefficients:
>> > Estimate std. err. t ratio Pr(>|t|)
>> > constant 0.093105 0.005238 17.77 < 2.22e-16
>> >
>> > Approximate significance of smooth terms:
>> > edf chi.sq p-value
>> > s(t1r) 1.833 24.153 0.00014213
>> >
>> > R-sq.(adj) = 0.435 Deviance explained = 47.1%
>> > GCV score = 0.0010938 Scale est. = 0.00099053 n = 30

    ronggui> are you using the mgcv package? if you are,just     ronggui> use a.fit$aic to get the aic.

hmm, yes, and no:

It's true what you say,
BUT is not at all recommended in general:

You should use the generic AIC() function rather than extracting components yourself.

This is a general priniciple: If possible use 'extractor functions' to work on objects rather then relying on internal representations.

This is particularly relevant for fitted models:

Do use residuals(.), fitted(.), LogLik(.), AIC(.), vcov(.) etc etc!

Now back to this problem:

>> AIC(a.fit)
>> Error in logLik(object) : no applicable method for "logLik"

I can't reproduce this; Eun definitely needs to give more details, since the following works fine:

> library(mgcv)
> x <- 1:50
> set.seed(1)
> y <- 2^(sin(x/10) + rnorm(50))
> a.fit <- gam(y ~ s(x), family = gaussian(link="log"))
> summary(a.fit)

Family: gaussian
Link function: log

Formula:
y ~ s(x)

Parametric coefficients:

            Estimate Std. Error t value Pr(>|t|)  
(Intercept)   0.3171     0.1251   2.535   0.0147 *
---
Signif. codes:  ..........{UTF-8 code}

Approximate significance of smooth terms:
       edf Est.rank    F p-value   
s(x) 2.858    9.000 3.07 0.00576 **
---
Signif. codes: ............

R-sq.(adj) =    0.4   Deviance explained = 43.5%
GCV score = 0.94391   Scale est. = 0.87107   n = 50

> AIC(a.fit)
[1] 140.6937
>

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Received on Mon Oct 24 18:01:48 2005

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