Re: [R] Factor variables with GAM models
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 form
E(Y | X1, X2, ...) = So + S(X1) + S(X2) + ...
where S(X) is a smooth function of X.
But there is absolutely nothing wrong with including factors in
mgcv::gam - they get expanded into the usually dummy variables depending
on the current contrasts as part of the model set-up routines just like
they do in lm(). Perhaps semiparametric might be a better description of
such a model but at least one implementation of GAMs in R can certainly
handle factors.
I haven't used gam::gam so can't comment on that and the OP doesn't say
which gam he is using.
HTH
G
Hence you might want to rethink why you'd want a
factor variable as a predictor variable in a GAM.
This is why the gam machinery doesn't just do the
factor conversion to indicator variables as is done in
lm.
HTH
Steven McKinney
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf
Of Noah Silverman [n...@smartmediacorp.com]
Sent: March 19, 2010 12:54 PM
To: r-help@r-project.org
Subject: [R] Factor variables with GAM models
I'm just starting to learn about GAM models.
When using the lm function in R, any factors I have in my data set are
automatically converted into a series of binomial variables.
For example, if I have a data.frame with a column named color and values
red, green, blue. The lm function automatically replaces it with
3 variables colorred, colorgreen, colorblue which are binomial {0,1}
When I use the gam function, R doesn't do this so I get an error.
1) Is there a way to ask the gam function to do this conversion for me?
2) If not, is there some other tool or utility to make this data
transformation easy?
3) Last option - can I use lm to transform the data and then extract it
into a new data.frame to then pass to gam?
Thanks!!!
__
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Re: [R] Factor variables with GAM models
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 `a' is a
factor, `x' is a continuous (or just metric) variable and `y' is a
response
y ~ a + s(x)
will fit a model where `a' is treated exactly as a factor variable is treated
by `lm', while `x' is smoothed over. In mgcv:gam then
y ~ s(x,by=a)
would create a `smooth-factor interaction' --- a separate smooth of `x' for
each level of `a'.
y ~ s(x,by=a,id=1)
would do the same, but would insist on each of the smooths of `x' having the
same smoothng parameter. ?gam.models gives some more detail.
best,
Simon
On Friday 19 March 2010 19:54, Noah Silverman wrote:
I'm just starting to learn about GAM models.
When using the lm function in R, any factors I have in my data set are
automatically converted into a series of binomial variables.
For example, if I have a data.frame with a column named color and values
red, green, blue. The lm function automatically replaces it with
3 variables colorred, colorgreen, colorblue which are binomial {0,1}
When I use the gam function, R doesn't do this so I get an error.
1) Is there a way to ask the gam function to do this conversion for me?
2) If not, is there some other tool or utility to make this data
transformation easy?
3) Last option - can I use lm to transform the data and then extract it
into a new data.frame to then pass to gam?
Thanks!!!
__
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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.
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+44 1225 386603 www.maths.bath.ac.uk/~sw283
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Re: [R] Factor variables with GAM models
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, landcover.class.C and
assign a value of 1 or 100% for elements belonging to that class or of 0
for elements not belonging.
That help some time.
Regards
Noah Silverman wrote:
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-linear. Hence my interest
in GAM. I suppose one alternative would be to try some polynomial
transformation on the variable as part of a Poisson model.
Any other suggestions would be welcome.
Thanks!
-N
On 3/19/10 8:37 PM, 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 form
E(Y | X1, X2, ...) = So + S(X1) + S(X2) + ...
where S(X) is a smooth function of X.
Hence you might want to rethink why you'd want a
factor variable as a predictor variable in a GAM.
This is why the gam machinery doesn't just do the
factor conversion to indicator variables as is done in
lm.
HTH
Steven McKinney
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On
Behalf Of Noah Silverman [n...@smartmediacorp.com]
Sent: March 19, 2010 12:54 PM
To: r-help@r-project.org
Subject: [R] Factor variables with GAM models
I'm just starting to learn about GAM models.
When using the lm function in R, any factors I have in my data set are
automatically converted into a series of binomial variables.
For example, if I have a data.frame with a column named color and values
red, green, blue. The lm function automatically replaces it with
3 variables colorred, colorgreen, colorblue which are binomial {0,1}
When I use the gam function, R doesn't do this so I get an error.
1) Is there a way to ask the gam function to do this conversion for me?
2) If not, is there some other tool or utility to make this data
transformation easy?
3) Last option - can I use lm to transform the data and then extract it
into a new data.frame to then pass to gam?
Thanks!!!
__
R-help@r-project.org mailing list
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.
__
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https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
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and provide commented, minimal, self-contained, reproducible code.
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Global Climate Change and Biodiversity
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University of York, York, YO10 5YW, UK
Phone: + 44 (0) 1904 328645, E-mail: ct...@york.ac.uk
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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[R] Factor variables with GAM models
I'm just starting to learn about GAM models.
When using the lm function in R, any factors I have in my data set are
automatically converted into a series of binomial variables.
For example, if I have a data.frame with a column named color and values
red, green, blue. The lm function automatically replaces it with
3 variables colorred, colorgreen, colorblue which are binomial {0,1}
When I use the gam function, R doesn't do this so I get an error.
1) Is there a way to ask the gam function to do this conversion for me?
2) If not, is there some other tool or utility to make this data
transformation easy?
3) Last option - can I use lm to transform the data and then extract it
into a new data.frame to then pass to gam?
Thanks!!!
__
R-help@r-project.org mailing list
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.
Re: [R] Factor variables with GAM models
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 smooth function of X.
Hence you might want to rethink why you'd want a
factor variable as a predictor variable in a GAM.
This is why the gam machinery doesn't just do the
factor conversion to indicator variables as is done in
lm.
HTH
Steven McKinney
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of
Noah Silverman [n...@smartmediacorp.com]
Sent: March 19, 2010 12:54 PM
To: r-help@r-project.org
Subject: [R] Factor variables with GAM models
I'm just starting to learn about GAM models.
When using the lm function in R, any factors I have in my data set are
automatically converted into a series of binomial variables.
For example, if I have a data.frame with a column named color and values
red, green, blue. The lm function automatically replaces it with
3 variables colorred, colorgreen, colorblue which are binomial {0,1}
When I use the gam function, R doesn't do this so I get an error.
1) Is there a way to ask the gam function to do this conversion for me?
2) If not, is there some other tool or utility to make this data
transformation easy?
3) Last option - can I use lm to transform the data and then extract it
into a new data.frame to then pass to gam?
Thanks!!!
__
R-help@r-project.org mailing list
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.
__
R-help@r-project.org mailing list
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.
Re: [R] Factor variables with GAM models
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-linear. Hence my interest in GAM.
I suppose one alternative would be to try some polynomial transformation
on the variable as part of a Poisson model.
Any other suggestions would be welcome.
Thanks!
-N
On 3/19/10 8:37 PM, 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 form
E(Y | X1, X2, ...) = So + S(X1) + S(X2) + ...
where S(X) is a smooth function of X.
Hence you might want to rethink why you'd want a
factor variable as a predictor variable in a GAM.
This is why the gam machinery doesn't just do the
factor conversion to indicator variables as is done in
lm.
HTH
Steven McKinney
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of
Noah Silverman [n...@smartmediacorp.com]
Sent: March 19, 2010 12:54 PM
To: r-help@r-project.org
Subject: [R] Factor variables with GAM models
I'm just starting to learn about GAM models.
When using the lm function in R, any factors I have in my data set are
automatically converted into a series of binomial variables.
For example, if I have a data.frame with a column named color and values
red, green, blue. The lm function automatically replaces it with
3 variables colorred, colorgreen, colorblue which are binomial {0,1}
When I use the gam function, R doesn't do this so I get an error.
1) Is there a way to ask the gam function to do this conversion for me?
2) If not, is there some other tool or utility to make this data
transformation easy?
3) Last option - can I use lm to transform the data and then extract it
into a new data.frame to then pass to gam?
Thanks!!!
__
R-help@r-project.org mailing list
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.
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
