# glmer categorical fixed effects estimate missing? -R

I posted a question recently regarding general linear mixed effects models, and I think I may have finally specified the glmer model correctly. I am interested in finding any differences in home range sizes between parks, between 2 time periods (wet and dry season). I have added individual ID as a random effect and year nested within ID as some IDs have multiple measurements for different years. I have 8 levels for "season", and 2 levels for "park". My question is: why is the 8th level for "season" omitted from the fixed estimates? Also, by looking at the p values, am I correct in stating that home range sizes for season 3 are significantly different between parks? I am definitely new to mixed models and would appreciate any help.

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: gaussian  ( log )
Formula: homerange ~ park * season + (1 | ID/year)
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

AIC      BIC   logLik deviance df.resid
2271.9   2332.7  -1117.0   2233.9      162

Scaled residuals:
Min      1Q  Median      3Q     Max
-3.7332 -0.4334 -0.0220  0.3912  4.1532

Random effects:
Groups   Name        Variance Std.Dev.
year:ID  (Intercept)  275.2   16.59
ID       (Intercept)  123.1   11.10
Residual             1997.7   44.70
Number of obs: 181, groups:  year:ID, 38; ID, 17

Fixed effects:
Estimate Std. Error t value Pr(>|z|)
(Intercept)    4.07652    0.10395  39.217  < 2e-16 ***
park1          0.35882    0.10381   3.456 0.000548 ***
season1       -0.08571    0.13749  -0.623 0.533024
season2        0.06915    0.12411   0.557 0.577380
season3        0.04081    0.12784   0.319 0.749554
season4        0.51415    0.20602   2.496 0.012574 *
season5        0.01990    0.13948   0.143 0.886525
season6       -0.55669    0.22691  -2.453 0.014152 *
season7       -0.32262    0.18747  -1.721 0.085256 .
park1:season1 -0.22670    0.13761  -1.647 0.099482 .
park1:season2 -0.18563    0.12389  -1.498 0.134042
park1:season3 -0.25638    0.12789  -2.005 0.045005 *
park1:season4  0.92146    0.20639   4.465 8.02e-06 ***
park1:season5 -0.17337    0.13925  -1.245 0.213114
park1:season6 -0.10128    0.22720  -0.446 0.655756
park1:season7  0.10990    0.18755   0.586 0.557901
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

• how did you estimate your model? If I use  family = gaussian("log") within glmer, I get the error, "response must be numeric". Maybe you can add a line with the command to your question? Nov 28, 2018 at 11:06