Great! Your suggestions made perfect sense and worked well. Thank you so much.
> On Jan 18, 2019, at 3:33 AM, Phillip Alday <phillip.alday at mpi.nl>
wrote:
>
> (once again with the list)
>
> Hi Caroline,
>
> This question is probably better suited to r-sig-mixed-models
> (https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models). Some things
> are hard to tell without better understanding your design (I am not an
> ecologist/relevant type of biologist), but I'll give it a go.
>
> I suspect that your model is over-parameterized. It's very rare to see
a
> factor occur both as a fixed effect and as a grouping variable (the
> stuff behind the | ) in the random effects.
>
> If you don't care about particular sites but rather only the general
> pattern across sites, then I would start with the model:
>
> wrack.biomass ~ year + (1 + year | site/trans)
>
> This treats site as a known source of variance, but not one that you
> care about estimating particular effects for. You can still extract
> predictions for them, i.e. the BLUPs, via coef(wrackbio), but their
> theoretical interpretation is a bit different than the other option below.
>
> If you do care about particular sites, I would use the model
>
> # if your transects are uniquely labeled across sites
> wrack.biomass ~ year * site + (1 | trans)
> # if the transect labels are only unique within sites
> wrack.biomass ~ year * site + (1 | sites:trans)
>
> This will give you fixed effects as in your model, but models the
> transects as a source of repetition and hence variance due to that
> grouping. The choice of exact specification depends on the labeling in
> your dataset; the sites:trans just guarantees unique labelling. The
> random effect in this case would estimate the average variance across
> all sites due to transects.
>
> Best,
> Phillip
>
>
>
>
> On 16/01/19 12:00, r-help-request at r-project.org wrote:
>> Send R-help mailing list submissions to
>
>> Today's Topics:
>>
>> 6. Nested mixed effectts question (Caroline)
>> ----------------------------------------------------------------------
>> Hi,
>>
>> I am helping a friend with an analysis for a study where she sampled
> wrack biomass in 15 different sites across three years. At each site,
> she sampled from three different transects. She is trying to estimate
> the effect of year*site on biomass while accounting for the nested
> nature (site/transcet) and repeated measure study design.
>>
>> wrack.biomass ~ year * site + (1 | site/trans)
>>
>> However she gets the following warning messages:
>> Warning messages:
>> 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
control$checkConv, :
>> unable to evaluate scaled gradient
>> 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
control$checkConv, :
>> Hessian is numerically singular: parameters are not uniquely
determined
>>
>> And her model output is:
>>
>>> summary(wrackbio)
>> Linear mixed model fit by REML
>> t-tests use Satterthwaite approximations to degrees of freedom
> ['lmerMod']
>> Formula: (actual.mean.biomass.m2.50.m.transect) ~ year * site + (1 |
> site/trans)
>> Data: wrack_resp_allyrs_transname
>>
>> REML criterion at convergence: 691
>>
>> Scaled residuals:
>> Min 1Q Median 3Q Max
>> -3.3292 -0.2624 -0.0270 0.1681 3.8024
>>
>> Random effects:
>> Groups Name Variance Std.Dev.
>> trans:site (Intercept) 0.0000 0.0000
>> site (Intercept) 0.5531 0.7437
>> Residual 94.6453 9.7286
>> Number of obs: 132, groups: trans:site, 44; site, 15
>>
>> Fixed effects:
>> Estimate Std. Error df t value Pr(>|t|)
>> (Intercept) 9.692e+00 5.666e+00 1.119e-04 1.711 0.999
>> year2016 1.256e+01 7.943e+00 8.700e+01 1.582 0.117
>> year2017 2.395e+00 7.943e+00 8.700e+01 0.302 0.764
>> siteCL 5.672e+01 8.013e+00 1.119e-04 7.079 0.999
>> siteDO -4.315e+00 8.013e+00 1.119e-04 -0.539 0.999
>> siteFL 7.872e+00 8.013e+00 1.119e-04 0.982 0.999
>> siteFS -7.619e+00 8.013e+00 1.119e-04 -0.951 0.999
>> siteGH 4.369e+00 8.013e+00 1.119e-04 0.545 0.999
>> siteLB -3.747e+00 8.013e+00 1.119e-04 -0.468 0.999
>> siteLBP -5.298e+00 8.943e+00 1.736e-04 -0.592 0.999
>> siteNB -2.953e+00 8.013e+00 1.119e-04 -0.369 1.000
>> siteNS 1.005e+00 8.013e+00 1.119e-04 0.125 1.000
>> sitePC -5.238e+00 8.013e+00 1.119e-04 -0.654 0.999
>> siteSB -7.649e+00 8.013e+00 1.119e-04 -0.955 0.999
>> siteSILT -4.734e+00 8.013e+00 1.119e-04 -0.591 0.999
>> siteSL -7.890e+00 8.013e+00 1.119e-04 -0.985 0.999
>> siteUD -8.230e+00 8.013e+00 1.119e-04 -1.027 0.999
>> year2016:siteCL -6.359e+01 1.123e+01 8.700e+01 -5.660 1.91e-07 ***
>> year2017:siteCL -5.210e+01 1.123e+01 8.700e+01 -4.638 1.23e-05 ***
>> year2016:siteDO -1.550e+01 1.123e+01 8.700e+01 -1.380 0.171
>> year2017:siteDO -3.022e+00 1.123e+01 8.700e+01 -0.269 0.789
>> year2016:siteFL -7.522e+00 1.123e+01 8.700e+01 -0.670 0.505
>> year2017:siteFL -1.167e+01 1.123e+01 8.700e+01 -1.039 0.302
>> year2016:siteFS -1.391e+01 1.123e+01 8.700e+01 -1.238 0.219
>> year2017:siteFS -2.170e+00 1.123e+01 8.700e+01 -0.193 0.847
>> year2016:siteGH -9.135e+00 1.123e+01 8.700e+01 -0.813 0.418
>> year2017:siteGH -4.031e+00 1.123e+01 8.700e+01 -0.359 0.721
>> year2016:siteLB -8.668e+00 1.123e+01 8.700e+01 -0.772 0.442
>> year2017:siteLB -1.530e+00 1.123e+01 8.700e+01 -0.136 0.892
>> year2016:siteLBP -5.336e+00 1.256e+01 8.700e+01 -0.425 0.672
>> year2017:siteLBP -1.826e+00 1.256e+01 8.700e+01 -0.145 0.885
>> year2016:siteNB -7.999e+00 1.123e+01 8.700e+01 -0.712 0.478
>> year2017:siteNB -5.645e+00 1.123e+01 8.700e+01 -0.502 0.617
>> year2016:siteNS -8.871e+00 1.123e+01 8.700e+01 -0.790 0.432
>> year2017:siteNS -3.443e+00 1.123e+01 8.700e+01 -0.306 0.760
>> year2016:sitePC -1.603e+01 1.123e+01 8.700e+01 -1.427 0.157
>> year2017:sitePC -2.955e+00 1.123e+01 8.700e+01 -0.263 0.793
>> year2016:siteSB -1.316e+01 1.123e+01 8.700e+01 -1.171 0.245
>> year2017:siteSB -3.220e+00 1.123e+01 8.700e+01 -0.287 0.775
>> year2016:siteSILT -1.616e+01 1.123e+01 8.700e+01 -1.438 0.154
>> year2017:siteSILT -2.497e-01 1.123e+01 8.700e+01 -0.022 0.982
>> year2016:siteSL -1.004e+01 1.123e+01 8.700e+01 -0.894 0.374
>> year2017:siteSL 1.123e+00 1.123e+01 8.700e+01 0.100 0.921
>> year2016:siteUD -1.345e+01 1.123e+01 8.700e+01 -1.197 0.235
>> year2017:siteUD 3.810e+00 1.123e+01 8.700e+01 0.339 0.735
>> ---
>> Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
>>
>> Correlation matrix not shown by default, as p = 45 > 12.
>> Use print(x, correlation=TRUE) or
>> vcov(x) if you need it
>>
>> convergence code: 0
>> unable to evaluate scaled gradient
>> Hessian is numerically singular: parameters are not uniquely determined
>>
>> Is the model unable to converge because her dataset is too small to
> include an interaction term or is stemming from issues of model structure?
>>
>> Thanks!
>>
>> Caroline
>>
>