On 11/05/16 17:11, Dominik Schneider wrote: > Hi Simon, Thanks for this explanation. > To make sure I understand, another way of explaining the y axis in my > original example is that it is the contribution to snowdepth relative > to the other variables (the example only had fsca, but my actual case > has a couple others). i.e. a negative s(fsca) of -0.5 simply means > snowdepth 0.5 units below the intercept+s(x_i), where s(x_i) could > also be negative in the case where total snowdepth is less than the > intercept value. > - Yes, this looks right.
> The use of by=fsca is really useful for interpreting the marginal > impact of the different variables. With my actual data, the term > s(fsca):fsca is never negative, which is much more intuitive. Is it > appropriate to compare magnitudes of e.g. s(x2):x2 / mean(x2) and > s(x2):x2 / mean(x2) where mean(x_i) are the mean of the actual data? > - I guess so (similarly to lm/glm). > Lastly, how would these two differ: s(x1,by=x2); or > s(x1,by=x1)*s(x2,by=x2) since interactions are surely present and i'm > not sure if a linear combination is enough. > - you'd probably use te(x1,x2) unless x1 and x2 are really on the same scale, in which case s(x1,x2) might be appropriate. The `by' variable trick is probably not going to work so well for interactions, however (it's not so clear what the by variable should be). -- Simon Wood, School of Mathematics, University of Bristol BS8 1TW UK +44 (0)117 33 18273 http://www.maths.bris.ac.uk/~sw15190 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
