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I have ran a glmm in r using glmmTMB from the glmmTMB package because my y variable (CVbetween) is from a beta distribution so I need to use family = beta_family.

When I look at the results with summary(model) It shows the results for the intercepts of certain fixed effects but it only show some results which is leaving me confused. For example, I have two options for the fixed effect "Method" it can be either Traditional or Novel but when looking at the results of the model it shows

                              Estimate Std. Error z value  Pr(>|z|) 
(Intercept)                 -2.687e+00  5.253e-01  -5.116  3.12e-07 ***
MethodTraditional            2.317e+00  2.978e-01   7.779  7.29e-15 ***

Why does itinclude one of the two options shouldn't it just say

 Estimate Std. Error z value  Pr(>|z|) 
(Intercept)                 -2.687e+00  5.253e-01  -5.116  3.12e-07 ***
Method                       2.317e+00  2.978e-01   7.779  7.29e-15 ***

Code for model


model2 <- glmmTMB(CVbetween_n ~ Method * Known_Age + Length:Known_Age + Weight:Known_Age + cross2:Known_Age + (1|ID), data=data, family = beta_family)
model2
summary(model2)

It has the same issue for when I use cross2 as a fixed effect. There are three cross options "AT X AK" "AT X CK" "AK X CK"

but the results show values for

Known_Age:cross2AT X AK     
Known_Age:cross2AT X CK

this is super odd to me. Please let me know if I have done anything wrong.

confusedindividual
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  • If the fixed effect Method has two levels, then Novel is the base level and the value for Traditional is the slope difference to the base. The same for cross2, note that the levels are in alphabetic order, the base being the 1st. But this seems to be more a question about statistics than about R code, post to [Cross Validated](https://stats.stackexchange.com/)? – Rui Barradas Aug 14 '22 at 04:55
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    I have closed this as a duplicate. This is the "one of my factor levels is missing" that we usually see brought up for `lm` output, but it's the same concept with `glm` and `glmm`. The link will show you why, whenever you have a factor variable in a regression, one of these is used for the intercept and is not reported separately. You can see this with `summary(lm(Sepal.Length ~ Species, iris))` in the built in `iris` data set, where the "setosa" species seems to be missing, but in fact the intercept is the mean value for `Sepal.Length` in the setosa species. – Allan Cameron Aug 14 '22 at 13:13

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