Hi Colin,
The GAMLSS package allows modelling of the response variable distribution
using either Exponential family or non-Exponential family distributions.
It also allows modelling of the scale parameter
(and hence the dispersion parameter for Exponential family distributions)
using explanatory
If you use GCV smoothness selection then, in the Gaussian case, the key
assumptions are constant variance and independence. As with linear
modelling, the normality assumption only comes in when you want to find
confidence intervals or p-values. (The GM Thm does not require normality
btw. but I do
>>> The default functional link for mgcv::gam is "log", so I doubt that
>> your theoretical understanding applies to GAM's in general. When Simon
>> Wood wrote his book on GAMs his first chapter was on linear models, his
>> second chapter was on generalized lienar models at which point he had
>> wr
On Nov 6, 2013, at 5:44 PM, Collin Lynch wrote:
>> The default functional link for mgcv::gam is "log", so I doubt that
> your theoretical understanding applies to GAM's in general. When Simon
> Wood wrote his book on GAMs his first chapter was on linear models, his
> second chapter was on general
> The default functional link for mgcv::gam is "log", so I doubt that
your theoretical understanding applies to GAM's in general. When Simon
Wood wrote his book on GAMs his first chapter was on linear models, his
second chapter was on generalized lienar models at which point he had
written over
On Nov 6, 2013, at 12:46 PM, Collin Lynch wrote:
> Greetings, My question is more algorithmic than prectical. What I am
> trying to determine is, are the GAM algorithms used in the mgcv package
> affected by nonnormally-distributed residuals?
>
> As I understand the theory of linear models the
Greetings, My question is more algorithmic than prectical. What I am
trying to determine is, are the GAM algorithms used in the mgcv package
affected by nonnormally-distributed residuals?
As I understand the theory of linear models the Gauss-Markov theorem
guarantees that least-squares regression
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