LAURA WEIR <0611792W <at> student.gla.ac.uk> writes: > > Hello everyone, > > I'm quite new to R and am trying to run a logistic model > to look at how various measures of boldness in > individual animals influences probability of capture, > however I also want to include random effects and > I'm not sure how to construct a model that incorporates both > of these things. > > Data was collected from 6 different groups of 6 individuals > with 10 replicates for each group. Whether an > individual was caught in a trial or not was called "Catchability" > and the outcome is binomial (caught = 1, > not caught = 0). "Catchability" is my response variable that > I want to see if I can explain by the other > variables. "Individual" and "Group" would be my random effects. > I have already installed lme4 but not > sure how to code for the model I need, can anyone offer any help please? > > The column headings in my data table are as follows. > > [1] "Trial" "Group" "Individual" "Mark" > [5] "Catchability" "Mboldness1" "Mboldness2" "Nboldness1" > [9] "Nboldness2" "Standardlength" > > This is the string of my data: > > str(data) > 'data.frame': 36 obs. of 10 variables: > $ Trial : int 1 2 3 4 5 6 7 8 9 10 ... > $ Group : int 1 1 1 1 1 1 2 2 2 2 ... > $ Individual : int 1 2 3 4 5 6 7 8 9 10 ... > $ Mark : Factor w/ 8 levels " - O"," - P",..: > 8 2 4 6 3 1 6 3 1 5 ... > $ Catchability : int 1 1 1 1 0 1 1 1 1 1 ... > $ Mboldness1 : int 7 10 0 11 15 5 15 4 15 1 ... > $ Mboldness2 : int 1 270 600 1 10 203 10 230 1 580 ... > $ Nboldness1 : int 0 0 0 0 0 0 1 0 2 0 ... > $ Nboldness2 : int 270 110 50 50 90 70 130 90 260 220 ... > $ Standardlength: num 40.5 37.4 38.6 41.1 39.1 40.5 50.2 60.3 53.9 55 ... >
I'm a little confused about your experimental design. You say you have 10 replicates per group, but I only see 36 observations in your data set -- from your description I would have expected 360? If you had 10 replicates per group I would say should do something like glmer(Catchability~[fixed effect predictors]+(1|Group/Individual), family=binomial,data=data) A couple of other points: * it's considered bad practice to name your data set 'data' (which is also the name of a built-in R function). Usually it's OK, but occasionally it could cause problems. * A rule of thumb is that you should not try to fit a model with more than k=N/10 parameters, where for binary data 'N' is the minimum of (number of successes, number of failures). * 6 groups is at the lower edge of feasibility for fitting a random effect -- you may find that glmer estimates the group-level variance as zero. (Don't panic.) I would suggest that you send follow-ups to r-sig-mixed-mod...@r-project.org and possibly take a look at <http://glmm.wikidot.com/faq> cheers Ben Bolker ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
