On Fri, 2010-03-19 at 20:37 -0700, Steven McKinney wrote:
> Hi Noah
>
> GAM models were developed to assess the functional form
> of the relationship of continuous predictor variables to the
> response, so weren't really meant to handle factor variables
> as predictor variables. GAMs are of the f
It doesn't usually make much sense to *smooth* over a factor variable (in the
cases where it does you should treat the factor as a random effect), but
there is no problem in including factor variables in a GAM. `gam' lets you
mix factor and continuous variables in a bunch of ways. Suppose that `
You can some time manually substitute a categorical variable with a set
of continuous variables.
For example, you have the variables like "landcover.class" with 3 values
"class A, class B, class C". You cna transform it into 3 continuous
variables landcover.class.A, landcover.class.B, landcove
Steve,
I get that. What you wrote make sense.
My challenge is the data I'm attempting to model. Some of the variables
are continuous, some are factors. both linear and poisson models work.
(Poisson doing a much more accurate job.) However, some of the
numerical variables are clearly non-l
Hi Noah
GAM models were developed to assess the functional form
of the relationship of continuous predictor variables to the
response, so weren't really meant to handle factor variables
as predictor variables. GAMs are of the form
E(Y | X1, X2, ...) = So + S(X1) + S(X2) + ...
where S(X) is a smoo
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