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Hi, I am trying to build GAM with linear constraints, for a general link function, not only identity. If I understand it correctly, the function pcls() can solve the problem, if the smoothness penalties are given. What I need is to incorporate the constraints before calculating the penalties. Can this be done in R? Any help would be greately appreciated. -- View this message in context:

Hi, I am working with CART regression now to predict a probability; the response is binary. Could anyone tell me in which cases it is better to use mean square error for splitting nodes and when mean absolute error should be preferred. I am now using the default (MSE) version and I can see that the obtained optimal tree is very different from the tree with the least mean absolute error. Thanks in

Hey, I make a regression for Gamma distribution with log link, in R and in SAS. In R, the dispersion is estimated by \phi=Deviance/(#_of_observations), In SAS, there are two options: \phi=Deviance/(#_of_observations-#_of_params) or \phi=Pearson/(#_of_observations-#_of_params). I understand that SAS formulae are correct, however, the coefficients, obtained by R and by the first version of SAS,