[R-sig-ME] error: model is nearly unidentifiable

[Ligia Pizzatto do Prado]

Hi there, I'm new to mixed models but have ran a few with success before. Now, while trying to analyse this new experiment I am  having an error that I quite don't understand...


The experiment is a two choice habitat ("choice": poor [0] vs rich [1]) for frogs under two-state treatments, lets say F and C. Then I have as potential variables frog size ("size"), air temperature ("temp"), humidity ("hum") and date of experiment (recorded as continuos variable starting at day 1...). This is a repeated measure design as frogs were tested both in F and C trials (thus id is my random effect). I want to know if the choice is affected by treat, but also considering size, temp, humidity, and date in my model.


First I did:


data$treat<- factor(data$treat)

data$id<- factor(data$id)

data$choice<- factor(data$choice)


summary(data)

treat        id     choice

C:24   1      : 2   0:24

F:24   2      : 2   1:24

           3      : 2

           4      : 2

           5      : 2

           6      : 2

      (Other):36



size                     temp                      hum                   date

 Min.   :35.70      Min.   :24.80       Min.   :53.00       Min.   : 1.00

 1st Qu.:38.25     1st Qu.:26.30     1st Qu.:58.50     1st Qu.: 4.00

 Median :42.40   Median :27.30   Median :63.00   Median : 9.00

 Mean   :42.02    Mean   :26.74     Mean   :63.65    Mean   :10.75

 3rd Qu.:44.27    3rd Qu.:27.40     3rd Qu.:70.50    3rd Qu.:16.75

 Max.   :51.00      Max.   :27.90     Max.   :76.00       Max.   :24.00

m1<- glmer(Fchoice ~ treat + SUL + temp + hum + date + (1|id), data = data, family = binomial)

Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?


I keep getting this message in all models except when I exclude both temp and hum, but I also get the message when tried null model: null<- glmer(choice ~ 1 + (1|id), data = data, family = binomial)


I tried to transform/re-scale all continuous variable (temp, hum, date) and nothing changed, and I quite don't understand why the error also appears in the null model, given id is a factor... If its a scale problem wouldn't this only appear in the continuous variables?


Can anyone provide some guidance here, please?


TIA,


Ligia


	[[alternative HTML version deleted]]