It was pretty depressing, except for the humorous/admirable fact that they rigorously included a footnote that documented the fact that they'd used a GAM and the high-order smoothers that resulted.
#Glims gams software#
I was in Costa Rica and saw some kind of study in a rainforest where some grad students had thrown some data into a GAM and accepted its crazy-complex smoothers because the software said so. You mention their use in ecology, which I have also noticed. With greater power comes greater responsibility. I'd emphasize that GAMs are much more flexible than GLMs, and hence need more care in their use. no "detective" work is needed on the part of the statistician'). They are essentially an extension of GLMs, however they are designed in a way that makes them particularly useful for uncovering nonlinear effects of numerical covariates, and for doing so in an "automatic" fashion (from Hastie and Tibshirani original article, they have 'the advantage of being completely automatic, i.e.
By combining the basis functions GAMs can represent a large number of functional relationship (to do so they rely on the assumption that the true relationship is likely to be smooth, rather than wiggly). cubic splines) and $q$ is the basis dimension. More in detail, while in (generalized) linear models the linear predictor is a weighted sum of the $n$ covariates, $\sum_^q \beta_i \, s_j \left( x_i \right)$, where the $s_1(\cdot),\dots,s_q(\cdot)$ are smooth basis functions (e.g. The main difference imho is that while "classical" forms of linear, or generalized linear, models assume a fixed linear or some other parametric form of the relationship between the dependent variable and the covariates, GAM do not assume a priori any specific form of this relationship, and can be used to reveal and estimate non-linear effects of the covariate on the dependent variable. I believe it's a valid statistical test, (and I see an increase in the use GAMs, at least in ecological journals), but I need to know better when its use is indicated over other regression analyses. I obviously don't have a great understanding of what a GAM does different than a GLM.
I realize this may be a potentially broad question, but I was wondering whether there are assumptions that indicate the use of a GAM (Generalized additive model) over a GLM (Generalized linear model)?
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