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

[Thierry Onkelinx]

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 op 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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>
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