Great! Your suggestions made perfect sense and worked well. Thank you so much.
> On Jan 18, 2019, at 3:33 AM, Phillip Alday <phillip.al...@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-requ...@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 >> > ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
