[R] GAM function with interaction

[Paul Simonin]

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)