Building General Linear Mixed Model in R

Question

I am trying to build a glmm in R but constantly get error messages (I am a complete beginner).

I have conducted an experiment with Camera traps in which I tested, if they react to a target, that I pulled in front of them, so my response variable is binomial. I try to build a GLMM, in which all the variables are fixed factors and day (in which the experiment was conducted is a random factor). Could anyone more experienced tell me what I am doing wrong (I first try only with one explanatory variable)?

I tried it with the glmm() and with lmer():

library(glmm)
set.seed(1234)
ptm <- proc.time()
Detections <- glmm(Detection ~ 0 + Camera, random = list(~ 0 + Day), 
    varcomps.names = c("Day"), data = data1, family.glmm = bernoulli.glmm, 
    m = 10^4, debug = TRUE)`

This one produces an obscenely large glmm, even with the minimal dataset.

and with

library(lme4)
Detections_glmm <- lmer(Detection ~ Camera + (1|Day), family="binomial")

This one gives the following error message:

Error in lmer(Detection ~ Camera + (1 | Day), family = "binomial") : 
  unused argument (family = "binomial")

Here is a minimal df:

data.frame(
    Detection = c(1, 0, 0, 1, 1, 1, 1, 0, 0, 0),
             Temperature = as.factor(c("10","10","10","10","10","20","20",
                                       "0","0","0")),
                Distance = as.factor(c("75","75","75","225","225","225",
                                       "75","150","150","150")),
                    Size = as.factor(c("0","0","0","0","1","1","1","1",
                                       "2","2")),
                   Light = as.factor(c("1","1","1","1","1","0","0","0",
                                       "0","0")),
                  Camera = as.factor(c("1","1","2","2","2","3","3","3",
                                       "1","1")),
                     Day = as.factor(c("1","1","1","2","2","2","3","3",
                                       "3","2"))

And Information about the variables:
Response variable:
Detection (binomial)

Explanatory variables:
Temperature of bottle: (0, 10, 20)
Distance from Camera (75, 150, 225)
Light(0/1)
Size of bottle (0, 1, 3)

I've never used `glmm`, but for `lme4` you need to use `glmer()` not `lmer()` to use a **generalized** lm with a link function. — Gregor Thomas, Feb 16 '23 at 20:37
I haven't done a "binomial" GLMM, but done many LMM's, and using Day as random factor will generally mean you need more than three levels. (I understand this may just be sample data, so this is just a heads up if you hadn't considered it.) https://peerj.com/articles/12794/ — MrSwaggins, Feb 17 '23 at 03:48
@BenBolker the model works correctly, if you change lmer to glmer as GregorThomas has suggested. — saimensis, Feb 22 '23 at 12:18
I know. These are all **comments**. I want someone to post an appropriate **answer** based on the comments ... — Ben Bolker, Feb 22 '23 at 12:28

score 0 · Answer 1 · answered Feb 26 '23 at 22:34

Using glmer should work. (lmer is only for linear mixed models, i.e. Gaussian responses.)

Detections_glmm <- glmer(Detection ~ Camera + (1|Day),    family="binomial")

If you really only have 3 days in your data set, you probably want to treat Day as a fixed rather than a random effect, see e.g. the GLMM FAQ or this CrossValidated question ...

Building General Linear Mixed Model in R

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