[R-sig-eco] Error message in GAM

[Ben Bolker]

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On 05/18/2011 06:16 PM, ARISTIDES LOPEZ wrote:
> Dear Dr. Gavin,
> 
> Thank you very much for your help. All my data are unique (because I have 56
> different stations). As you suggest I restrict the
> complexity of the individual smooths:
> 
> response ~ s(Lat, k = 9) + s(Long, k = 9) + s(deep, k = 9)
> 
> Problem solved.
> 
> Now I try to make other model:
> 
>  modelo2<-gam(Density~s(year, k=6)+s(Month, k=6)+s(rainfall, k=6),
> family=Gamma, data=at)
> 
> The "new" problem is that R give me the next error  *"Error en
> smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) :
>   A term has fewer unique covariate combinations than specified maximum
> degrees of freedom"*.
> 
> Anybody knows what mean this?
> 
> Regards.

  It means you're pushing your data too hard: how about being
old-fashioned and fitting quadratic models [e.g. poly(Lat,2)] for each
of your predictor variables (this of course ignores interactions, which
you might ?? want to worry about in some cases -- but you probably
can't.  In principle, gam() in the mgcv package (which is what I assume
you are using) tries to adjust the degree of complexity of your model
downward as appropriate, but it may be having a hard time doing so; can
you set k lower?  For the models that do succeed, I would suspect that
the effective degrees of freedom fitted are much lower than the k values
you are specifying, so you could afford to reduce them (see ?choose.k )

  Remember the rule of thumb that you should not be trying to fit more
than *at most* N/10 parameters, where N is your number of points -- so
quadratic models of 3 independent predictors (= 7 parameters, intercept
+ 2 for each predictor variable) would already be overfitting slightly.

  cheers
    Ben Bolker

> 
> 2011/5/18 Gavin Simpson <gavin.simpson at ucl.ac.uk>
> 
>> On Wed, 2011-05-18 at 10:53 -0500, ARISTIDES LOPEZ wrote:
>>> Dear members list,
>>>
>>> I'm trying to make a model for descrive the distribution of demersal
>> fishes
>>> in the Colombian Caribbean Sea. I have a data set of n= 56, the model is
>>> like this: Density (ind/km2) ~ s(Lat) + s(Long) + s(deep). The problem is
>>> that R give me the error message *"Model has more coefficients than
>> data"*.
>>>
>>> Anybody knows how can avoid this?
>>>
>>> Faithfully.
>>
>> Each of your smooths will be using k = 10 degrees of freedom so that is
>> 30 degrees of freedom already, which is a lot for a data set of 56
>> observations.
>>
>> Are all the data unique? i.e. you have 56 unique density values, 56
>> unique lats, 56 unique lons etc. If not, it might be the the unique
>> information in the data is not sufficient to support the complexity of
>> the smooths.
>>
>> My money would be on that you did something you haven't actually told
>> us, and have more smooths in the model than you say and they are using
>> more degrees of freedom than it appears to us.
>>
>> The easy way to try to solve the problem, will be to restrict the
>> complexity of the individual smooths:
>>
>> response ~ s(Lat, k = 6) + s(Long, k = 6) + s(deep, k = 6)
>>
>> for example.
>>
>> You could probably model these data as a Possion with an offset term for
>> the km2 covered by each sample, rather than treating these as a density.
>>
>> HTH,
>>
>> G
>>
>> --
>> %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
>>  Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
>>  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
>>  Pearson Building,             [e] gavin.simpsonATNOSPAMucl.ac.uk
>>  Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
>>  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
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>>
>>
> 
> 
> 
> 
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