- 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 -- View this message in context: http://r.789695.n4.nabble.com/te-interactions-and-AIC-model-selection-with-GAM-tp4638368p4638922.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.