Psychometric curves with glmm
Donald Edward Frederick <donald at ...> writes:
Hello all,
......
Rats (n=8) are trained to discriminate between two stimuli (A,B). Each rat is tested on four (4) different stimulus pairs ( (A1,B1),(A2,B2),...). Each rat is tested on each pair for three days. The stimulus pair presentation order is randomly selected. Each day consists of ~300 trials evenly split between the two stimuli. I would like to construct a psychometric curve of performance against sampling duration. The rats select their own 'sampling duration' for each presentation on each trial. I am interested in knowing if there is a general effect where stimulus sampling duration predicts performance (I expect a sigmoidal relationship). I also want to model the effects of the stimulus set, the stimulus (A,B), and day when accounting for the random effects (Subject, StimulusSet, Day).
.....
My current model is: glmer(Correct ~ SampleDuration+StimulusSet+Stimulus+Day+(StimulusSet:Day|Subject), family=binomial(link="logit"), data=g) When I run this, my results are not unexpected, but I'm always leery of this. I *think* that this model fits the fixed effects of
(SampleDuration,
StimulusSet,Stimulus, and Day). Additionally, I fit a
random intercept for
each subject and a random slope for each subject that varies by StimulusSet:Day. I expect that there are some individual differences
between the Subjects. I
also expect that there will be differences between
StimulusSets and Days.
Specifically, some StimulusSets are easier than others and there is learning from Day 1 to Day 3. Does this all make sense, or, have I ventured down a terrible path? Thanks Donald
A couple of thoughts. Is Correct a binary variable representing the response on individual trials, i.e., taking on two values, say 0/1? or is it either the proportion of correct responses or the number? If it is the proportion then you want to include a weights argument with the number of trials and if it is the number, then it would be better that Correct is a 2 column matrix with Correct and Incorrect columns. Is this a two-alternative forced-choice situation, i.e., would the sigmoids lower asymptote be expected to be at 0.5 instead of 0? If so, you might consider using the mafc.logit link function from the psyphy package but you would have to use the development version of lme4 for it to work. Since rats choose SampleDuration, do you want it to be a random slope term as well? Your random term, (StimulusSet:Day|Subject), gives the overall variability for subject within each combination of StimulusSet and Day. You might want to consider terms like (1 | Subject/Day) that give a random intercept for both Subject and Subject:Day. This is equivalent to having terms (1 | Subject) + (1 | Subject:Day). You would have to think more deeply as to how to include all of the terms in your experiment but that might get you started. Hope that is helpful. best, Ken
Kenneth Knoblauch Inserm U846 Stem-cell and Brain Research Institute Department of Integrative Neurosciences 18 avenue du Doyen L?pine 69500 Bron France tel: +33 (0)4 72 91 34 77 fax: +33 (0)4 72 91 34 61 portable: +33 (0)6 84 10 64 10 http://www.sbri.fr/members/kenneth-knoblauch.html