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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