Re: [R] logLik == -Inf in gls

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Fri 10 Feb 2006 - 00:24:02 EST

Please do not repeatedly post the same thing. This is the same as

https://stat.ethz.ch/pipermail/r-help/2006-February/086381.html

(except you remembered to sign that one).

You are fitting a weighted not a generalised least squares model: lm() will do that.

On Thu, 9 Feb 2006, nhy303@abdn.ac.uk wrote:

> I am trying to fit a generalised least squares model using gls in the nlme
> package.
>
> The model seems to fit very well when I plot the fitted values against the
> original values, and the model parameters have quite narrow confidence
> intervals (all are significant at p<5%).
>
> The problem is that the log likelihood is always given as -Inf. This
> doesn't seem to make sense because the model seems to fit my data so well.
> I have checked that the residuals are stationary using an adf test. I
> can't work out whether
> - the model really doesn't fit at all
> - there is something in my data that stops the implementation of logLik
> working correctly (the -Inf value says the calculation hasn't worked)
>
> Possible causes are:
> - There are lots of NAs in my data (model and response variables)
> - There is some autocorrelation in the data that is not accounted for by
> the model (most is accounted for).
>
> But, I've tried recreating the problem using a simpler data set, and have
> never found the same problem.

Well, how then do you expect us to be able to recreate it?

As a pure guess, look at your weights. Are any numob4150 zero?

> The command I use to fit the model is...
>
>
>
> result2 <- gls(lci4150 ~ propCapInStomachs +
> temperature +
> as.factor(monthNumber) +
> lagLci1 +
> lagcap1 +
> lagcap2,
> data = monthly,
> subset = subset1985,
> na.action = na.approx,
> weights = varFixed( ~ 1/numob4150)
> )
>
>
>
> The output I get is...
>
>
>
> Generalized least squares fit by REML
> Model: lci4150 ~ propCapInStomachs + temperature +
> as.factor(monthNumber) + lagLci1 + lagcap1 + lagcap2
> Data: monthly
> Subset: subset1985
> AIC BIC logLik
> Inf Inf -Inf
>
> Variance function:
> Structure: fixed weights
> Formula: ~1/numob4150
>
> Coefficients:
> Value Std.Error t-value p-value
> (Intercept) -0.3282412 0.5795665 -0.566356 0.5717
> propCapInStomachs 0.0093283 0.0039863 2.340107 0.0202
> temperature 0.4342514 0.1526104 2.845490 0.0048
> as.factor(monthNumber)2 0.3990717 0.3869991 1.031195 0.3036
> as.factor(monthNumber)3 1.3788334 0.3675690 3.751223 0.0002
> as.factor(monthNumber)4 1.4037195 0.3857764 3.638686 0.0003
> as.factor(monthNumber)5 0.9903316 0.3436177 2.882074 0.0043
> as.factor(monthNumber)6 0.3453741 0.3043698 1.134719 0.2577
> as.factor(monthNumber)7 0.3948442 0.3035142 1.300909 0.1946
> as.factor(monthNumber)8 0.5021812 0.3532413 1.421638 0.1565
> as.factor(monthNumber)9 -0.0794319 0.3598981 -0.220707 0.8255
> as.factor(monthNumber)10 0.3536805 0.3790538 0.933061 0.3518
> as.factor(monthNumber)11 0.7874834 0.3557116 2.213826 0.0278
> as.factor(monthNumber)12 0.1854279 0.3178320 0.583415 0.5602
> lagLci1 0.5488437 0.0576144 9.526151 0.0000
> lagcap1 0.0110994 0.0043669 2.541714 0.0117
> lagcap2 -0.0088080 0.0041099 -2.143127 0.0332
>
>
>
> Does anyone have any suggestions of how I can get a meaningful value for
> logLik? Or some other way that I can compare models.
>
> Thankyou,
>
> Lillian.
>

-- 
Brian D. Ripley,                  ripley@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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Received on Fri Feb 10 00:27:26 2006

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