[R] te( ) interactions and AIC model selection with GAM

[Ricardogg]

- About the visualization, my question is more about interpretation. In the
case of  :

 model_name <- gam ( bm  ~ t + te (t_year, temp_W, temp_sept, k = 5, bs = c(
“cc”,”cr”,”cr”)), data = data) 

* a)* vis.gam (model_name , view= c(“t_year”, “temp_W”))

*b)* vis.gam (model_name , view= c(“t_year”, “temp_sept”))

I imagine that what we see in a) is the effect on t_year by temp_W but  this
effect is also affected by temp-sept right? In other words, is what we see
the effect of temp_W on  t_year considering the other element of the model
which is temp-sept?

- About AIC model selection, my question is more focus on whether it is
necessary to use the same  the same type of smoother ( e.g te) and the same
dimension of the basis (k) for different model comparison...that is, if I
have a complex model in my list of models that I want to compare like:

gam ( bm ~ t + te (t_year, temp_W, temp_sept, k = 5, bs = c(
“cc”,”cr”,”cr”)), data = data) 

should I always use as type smoother te () instead of using s ( ) and always
fix the k as k = 5?? Here the maximum k I can put is k = 5.

for example, let's say I have another simpler model in my list of models I
want to compare in which I want to check the relationship between bm and t. 
Can I write it like /gam (bm ~ s(t, k = 13, bs = “cr”), data = data) /? Or I
have to write like/ gam (bm ~ te (t, k = 5, bs = “cr”), data = data)/ to do
the comparison in the same conditions for all the models?

Thank you very much for your response Simon,

Best regards,

Ricardo












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