[R-sig-ME] Complex model yields similar results to simpler model, but also warnings: can I ignore them?

[Jackie Wood]

Hi Thierry,

Thanks for the quick response! I've tried the following variance weight
structures:

vf1 <- varIdent (form = ~ 1 | cross*age_bin)

vf2 <- varExp (form = ~ age_bin | cross)

vf3 <- varComb (varIdent(form =~ 1 | cross), varExp (form = ~ age_bin))

vf4 <- varExp (form = ~ age_bin)

vf2 and vf3 yielded the same warnings as for the original model (using
vf1). vf4 ran fine but  I do think the data indicate that there are some
differences in heteroscedasticity among the crosses as well. The AIC value
was quite a bit lower for the model using vf1 but perhaps that is not
entirely trustworthy given the warnings that were generated.

I guess my original question stands about whether this warning means that
the model is bad news and I should go with something like varExp (form = ~
age_bin) regardless of which specification seems preferred via comparison
of AIC values. Or if, because all other relevant output stays fairly
consistent regardless of the variance weighting specification, I can
proceed with the more complex version.

Jackie





On Thu, Mar 31, 2016 at 10:53 AM, Thierry Onkelinx <thierry.onkelinx at inbo.be
> wrote:

> Dear Jackie,
>
> I presume that the heteroscedasticy along age_bin is somewhat smooth. In
> such case you use a less parametrised model the variance. Like e.g.
> varExp(form = ~age_bin|cross). ?varClasses gives an overview of available
> classes. Note that you can combine classes with varComb().
>
> Best regards,
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
> Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
>
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> 2016-03-31 16:14 GMT+02:00 Jackie Wood <jackiewood7 at gmail.com>:
>
>> Hello R-users,
>>
>> I'm attempting to model differences in fork length over time for 4
>> different cross types of a species of freshwater fish using the most
>> recent
>> version of nlme . Examining plots of the data, fork length increases
>> non-linearly over time so I've included a second order polynomial for age
>> such that the fixed effects portion of the model has the following
>> specification:
>>
>> model <- lme(FL ~ cross * age_bin + cross*I(age_bin^2)
>>
>> Plots of the random effects suggest evidence for random slopes with
>> respect
>> to family for age and age^2, and further these are correlated with the
>> intercept.
>>
>> So I specified the random effects part as:
>>
>> random = ~age_bin + I(age_bin^2)|fam
>>
>> Likelihood ratio tests do favor this random effects structure over simpler
>> structures.
>>
>> Plotting the residuals, variance in length definitely increases with
>> increasing age and also appears to vary per cross type so I added the
>> following variance weighting structure to the model:
>>
>> weights = varIdent(form = ~ 1|cross*age_bin))
>>
>> I've performed typical likelihood ratio tests which consistently favor the
>> model described above over other simpler model specifications (in terms of
>> random effects specifications, and variance weighting), but with the above
>> model I also get a few of these types of warnings:
>>
>> 1: In logLik.reStruct(object, conLin) :
>>   Singular precision matrix in level -1, block 15
>>
>> Searching online help forums the advice I see is that the model is likely
>> overparameterized, and indeed if I remove either the variance weighting
>> completely, or simplify the random effects to 1|fam (any random slope type
>> random effects specification gives the same warning), everything works
>> just
>> fine. I also checked the raw data which seems sound to me.
>>
>> I do feel as though the more complex random effects structure is warranted
>> from plotting the data and there is definitely heteroscedasticity to
>> account for. When I run the above model without the variance weights, the
>> resulting fixed effects coefficients and estimated random effects and
>> correlations have values that are pretty close to the model with variance
>> weights (the residual variance is of course different). So my question is
>> how important are the warnings? If the output seems reasonable and
>> corresponds pretty closely with the output of a simpler model that runs
>> just fine, is it justifiable to ignore the warnings or am I asking for
>> trouble?
>>
>> I mean, I could get rid of the variance weighting structure and simply
>> transform fork length, it does help the heteroscedasticity issue, but I do
>> find the variance interesting and transforming it away wouldn't be my
>> first
>> choice.
>>
>> I'd really appreciate your input!
>>
>> Jackie
>>
>> --
>> Jacquelyn L.A. Wood, PhD.
>> 224 Montrose Avenue
>> Toronto, ON
>> M6G 3G7
>> Phone: (514) 293-7255
>>
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>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
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>>
>
>


-- 
Jacquelyn L.A. Wood, PhD.
224 Montrose Avenue
Toronto, ON
M6G 3G7
Phone: (514) 293-7255

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