Should I use GLMM or GAM in my analysis? I am analyzing data on polar bears and trying to figure out if different variables influence their movement. My data has a mix of categorical (e.g. bear ID number) and numerical variables (e.g. bear age)
 
For my analysis, I was thinking of doing a model in a format like this:
 
Movement = x1*(year) + x2*(length of ice
season) + x3*(age of bear) + bear’s
individual ID + etc.
 
I am stuck between two options:
1.    Doing a GLMM (Generalized Linear Mixed Model). Since I’m pretty sure my independent variables don’t all have a linear relationship to my dependent variable, I was thinking of doing a quick visual analysis of my variables  and tweaking them accordingly: for example, if it looks like age of the bear has more of an exponential relationship with my movement variable, then I would write it in the model as x3*log(age of bear).
 
2.  Doing a GAM (Generalized Additive Model). I’m not too familiar with this type of model, but I have heard that it’s usually the way to go if you believe the relationship between your variables isn’t necessarily linear.
 
In both cases, I am planning on including the bear ID as a random effect.
 
Which test would you recommend? Are there pros and cons to each? As an aside, my data also has relatively small sample sizes (30 to 45 bears).
 A: Those aren't exclusive options. GL(M)M and GAM do different things despite the apparent similarities in names.
The choice of a GLM (generalized linear model) depends on the nature of the response variable and how you think the response is related to the linear predictor from the regression. For example, you would use a GLM for count or categorical outcomes, or if you think that the mean value of the response is linked to the logarithm of the entire linear predictor. If you want to model individual bears as random effects then you have a mixed model, potentially a GLMM (generalized linear mixed model).
A GAM (generalized additive model) is one way among others to structure the predictors to allow for flexibly modeled nonlinearities between predictors and outcomes. It sounds like flexibly modeled continuous predictors will be important for your application, so use of GAM or regression splines is worth investigating. You can combine a GAM or regression splines with a GL(M)M.
If your movement outcome is continuous and is expected to link directly to the linear predictor after the nonlinearities in the outcome-predictor associations are handled with a GAM or regression splines, then you don't need to use a generalized model. You would use a mixed model if you choose to treat the bears as random effects. That's not the only way to handle repeated measurements over time on individuals, however. Chapter 7 of Frank Harrell's course notes and book outlines the pros and cons of several approaches. He shows in detail how to use generalized least squares as an alternative that might work for your data.
Other portions of the Harrell references should help inform the best way to model your data.
