I will try to explain my data as good as possible. So we taged 13 different whales with a tag that records time, depth, speed, angle of descent and ascent 25 samples every second. The normal diving behaviour of these animals is one deep dive of one hour to 1200 meters followed by a series of 3-7 shallow dives of 20 minutes up to 300 m. Because the tag not always stays the same time in each animal, my data is unbalanced and some tag records have one deep dive and 6 shallow dives while other records have 7 deep dives and 26 shallow dives. I divided each dive in units of 30 seconds. for each unit I have the next data: whale number, dive number, total number of fluke strokes in the 30 seconds unit of analysis, mean of the sin of the angle during the 30 seconds unit, swim speed, dive type(ascent or descent), dive direction( if it is a descent or an ascent) and time since the start of the dive and finally my variable response which is presence or absence of one type of fluke stroke called stroke type B.
I think there has to be some autocorrelation between each 30 seconds unit of analyis and need to include it in my model but do not know how!
I am interested to know what affects the presence or absence of the type B stroke (which is a binomial variable with 0 and 1) so I decide to use a binomial glmm with whale number as a random effect. I included as well dive number within whale as a random effect.
here is the model.
glmm114<-lmer (StrokeB~ Time * Depth+SINP+flukes*Depth+speed+(1|whale_number)+(0+dive_number|whale_number),data=Luciadeepas, family = binomial)
this is my final model after taking out the non significative variables, the problem is that due to the interaction a problem appears saying
The false convergence warning message (8)
I looked on internet and it says is a common problem, and some people says that it doesnt make any change in the output while others says that each variable has to be divided by 100. but when I do this then the variables that become significant doesnt make any sense.