[R] Nested mixed effectts question

Caroline g||ddec@ @end|ng |rom @c|ence@oregon@t@te@edu
Fri Jan 18 16:01:52 CET 2019


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 using 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 using 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
>> 
> 



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