Hello R Users,
I have a question regarding fitting a model with GAM{mgcv}. I have data
from several predictor (X) variables I wish to use to develop a model to
predict one Y variable. I am working with ecological data, so have data
collected many times (about 20) over the course of two years. Plotting
data independently for each date there appears to be relationships
between Y (fish density) and at least several X variables (temperature
and light). However, the actual value of X variables (e.g., temperature)
changes with date/season. In other words, fish distribution is likely
related to temperature, but available temperatures change through the
season. Thus, when data from all dates are combined to create a model
from the entire dataset, I think I need to include some type of
metric/variable/interaction term to account for this date relationship.
I have written the following code using a "by" term:
Distribution.s.temp.logwm2.deltaT<-gam(yoyras~s(temp,by=datecode)+s(logwm2,by=datecode)+s(DeltaT,by=datecode),data=AllData)
However, I am not convinced this is the correct way to account for this
relationship. What do you think? Is there another way to include this in
my model? Maybe I should simply include date ("datecode") as another
term in the model?
I also believe there may be an interaction between temperature and
light (logwm2), and based on what I have read the "by" method may be the
best way to include this. Correct?
Thank you for any input, tips, or advice you may be able to offer. I am
new to R, so especially grateful!
Thanks again,
Paul Simonin
(PhD student)
PS- If you would like additional information let me know. Also, if this
question is inappropriate for the help list please let me know.
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