Stepwise algorithm for GAM

Hi,

there is no automatic variable selection in the mgcv package. You should 
remove the superfluous terms manually. You can choose them using ML-test 
, comparing AIC values or using plot function.

An example:

set.seed(3)
n<-200
## simulate data
dat <- gamSim(1,n=n,scale=.15,dist="poisson")
str(dat)
## spurious predictors
dat$x4 <- runif(n, 0, 1)
dat$x5 <- runif(n, 0, 1)

b1<-gam(y~s(x0)+s(x1)+s(x2)+s(x3)+s(x4)+s(x5),data=dat,family=poisson) # 
full model
summary(b1)  # you can choose superfluous predictors based on this output
b2<-gam(y~s(x0)+s(x1)+s(x2)+s(x3)+s(x4),data=dat, family=poisson) # 
reduced model without x5
anova(b,b2,test="Chisq") # comparing the two models

plot(b1,pages=1) # smooth function is a nearly horizontal line for 
superfluous predictors

Setting select=T may give more clear pattern, however in my toy-example 
the difference is small.

Best wishes

Zoltan

2011.05.24. 5:21 keltezssel, ARISTIDES LOPEZ rta:

Hello all,


Just a question, Im trying to fit my model throughout stepwise
selection.At this point (with the valuable help of Gavin and Ben) my
model are like
this:


model 1<-gam(Young (No. ind)~s(Lat, k=6)+s(Long, k=6)+s(Deep, k=6)+s(Area
(km2),k=6)+as.factor (year),family=poisson,data=L. synagris)


I have 4 species * 3 groups (young, adult and total) * 5 explanatory
variables (Lat, Lon, Deep, Area, Year). So Im looking for a stepwise
algorithm  that help me to select the best model. I tried with step () in
the stats package but R give me the following error message:


"Error en glm.control(irls.reg = 0, epsilon = 1e-06, maxit = 100, trace =
FALSE,  : el argumento(s) no fue utilizado(s) (irls.reg = 0, mgcv.tol =
1e-07, mgcv.half = 15,..............."


Any suggestion?


Cheers


Date: Wed, 18 May 2011 10:53:41 -0500
From: ARISTIDES LOPEZ<aristideslpz at gmail.com>
To: r-sig-ecology at r-project.org
Subject: [R-sig-eco] Error message in GAM
Message-ID:<BANLkTikz-dQ=jV9YkfTGgEYO5uBWmcUsMw at mail.gmail.com>
Content-Type: text/plain

Dear members list,

I'm trying to make a model for descrive the distribution of demersal fishes
in the Colombian Caribbean Sea. I have a data set of n= 56, the model is
like this: Density (ind/km2) ~ s(Lat) + s(Long) + s(deep). The problem is
that R give me the error message *"Model has more coefficients than data"*.

Anybody knows how can avoid this?

Faithfully.

--
Aristides Lpez-Pea



Date: Wed, 18 May 2011 17:48:04 +0100
From: Gavin Simpson<gavin.simpson at ucl.ac.uk>
To: ARISTIDES LOPEZ<aristideslpz at gmail.com>
Cc: r-sig-ecology at r-project.org
Subject: Re: [R-sig-eco] Error message in GAM
Message-ID:<1305737284.25148.15.camel at prometheus.geog.ucl.ac.uk>
Content-Type: text/plain; charset="UTF-8"

On Wed, 2011-05-18 at 10:53 -0500, ARISTIDES LOPEZ wrote:

Dear members list,

I'm trying to make a model for descrive the distribution of demersal

fishes

in the Colombian Caribbean Sea. I have a data set of n= 56, the model is
like this: Density (ind/km2) ~ s(Lat) + s(Long) + s(deep). The problem is
that R give me the error message *"Model has more coefficients than

data"*.

Anybody knows how can avoid this?

Faithfully.

Each of your smooths will be using k = 10 degrees of freedom so that is
30 degrees of freedom already, which is a lot for a data set of 56
observations.

Are all the data unique? i.e. you have 56 unique density values, 56
unique lats, 56 unique lons etc. If not, it might be the the unique
information in the data is not sufficient to support the complexity of
the smooths.

My money would be on that you did something you haven't actually told
us, and have more smooths in the model than you say and they are using
more degrees of freedom than it appears to us.

The easy way to try to solve the problem, will be to restrict the
complexity of the individual smooths:

response ~ s(Lat, k = 6) + s(Long, k = 6) + s(deep, k = 6)

for example.

You could probably model these data as a Possion with an offset term for
the km2 covered by each sample, rather than treating these as a density.

HTH,

G

--
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
  Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
  Pearson Building,             [e]
gavin.simpsonATNOSPAMucl.ac.uk<http://gavin.simpsonatnospamucl.ac.uk/>
  Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%




------------------------------

Message: 9
Date: Wed, 18 May 2011 17:16:10 -0500
From: ARISTIDES LOPEZ<aristideslpz at gmail.com>
To: r-sig-ecology at r-project.org, gavin.simpson at ucl.ac.uk
Subject: Re: [R-sig-eco] Error message in GAM
Message-ID:<BANLkTimUQhNjhdOX9LNNDdT60gSiWNX38w at mail.gmail.com>
Content-Type: text/plain

Dear Dr. Gavin,

Thank you very much for your help. All my data are unique (because I have 56
different stations). As you suggest I restrict the
complexity of the individual smooths:

response ~ s(Lat, k = 9) + s(Long, k = 9) + s(deep, k = 9)

Problem solved.

Now I try to make other model:

  modelo2<-gam(Density~s(year, k=6)+s(Month, k=6)+s(rainfall, k=6),
family=Gamma, data=at)

The "new" problem is that R give me the next error  *"Error en
smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) :
  A term has fewer unique covariate combinations than specified maximum
degrees of freedom"*.

Anybody knows what mean this?

Regards.

2011/5/18 Gavin Simpson<gavin.simpson at ucl.ac.uk>

On Wed, 2011-05-18 at 10:53 -0500, ARISTIDES LOPEZ wrote:

Dear members list,

I'm trying to make a model for descrive the distribution of demersal

fishes

in the Colombian Caribbean Sea. I have a data set of n= 56, the model is
like this: Density (ind/km2) ~ s(Lat) + s(Long) + s(deep). The problem

is

that R give me the error message *"Model has more coefficients than

data"*.

Anybody knows how can avoid this?

Faithfully.

Each of your smooths will be using k = 10 degrees of freedom so that is
30 degrees of freedom already, which is a lot for a data set of 56
observations.

Are all the data unique? i.e. you have 56 unique density values, 56
unique lats, 56 unique lons etc. If not, it might be the the unique
information in the data is not sufficient to support the complexity of
the smooths.

My money would be on that you did something you haven't actually told
us, and have more smooths in the model than you say and they are using
more degrees of freedom than it appears to us.

The easy way to try to solve the problem, will be to restrict the
complexity of the individual smooths:

response ~ s(Lat, k = 6) + s(Long, k = 6) + s(deep, k = 6)

for example.

You could probably model these data as a Possion with an offset term for
the km2 covered by each sample, rather than treating these as a density.

HTH,

G

--
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
  Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
  Pearson Building,             [e] gavin.simpsonATNOSPAMucl.ac.uk<http://gavin.simpsonatnospamucl.ac.uk/>
  Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%

--
Aristides Lpez-Pea

        [[alternative HTML version deleted]]



------------------------------

Message: 10
Date: Wed, 18 May 2011 18:28:20 -0400
From: Ben Bolker<bbolker at gmail.com>
To: r-sig-ecology at r-project.org
Subject: Re: [R-sig-eco] Error message in GAM
Message-ID:<4DD44804.1020705 at gmail.com>
Content-Type: text/plain; charset=ISO-8859-1

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

On 05/18/2011 06:16 PM, ARISTIDES LOPEZ wrote:

Dear Dr. Gavin,

Thank you very much for your help. All my data are unique (because I have

56

different stations). As you suggest I restrict the
complexity of the individual smooths:

response ~ s(Lat, k = 9) + s(Long, k = 9) + s(deep, k = 9)

Problem solved.

Now I try to make other model:

  modelo2<-gam(Density~s(year, k=6)+s(Month, k=6)+s(rainfall, k=6),
family=Gamma, data=at)

The "new" problem is that R give me the next error  *"Error en
smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) :
   A term has fewer unique covariate combinations than specified maximum
degrees of freedom"*.

Anybody knows what mean this?

Regards.

  It means you're pushing your data too hard: how about being
old-fashioned and fitting quadratic models [e.g. poly(Lat,2)] for each
of your predictor variables (this of course ignores interactions, which
you might ?? want to worry about in some cases -- but you probably
can't.  In principle, gam() in the mgcv package (which is what I assume
you are using) tries to adjust the degree of complexity of your model
downward as appropriate, but it may be having a hard time doing so; can
you set k lower?  For the models that do succeed, I would suspect that
the effective degrees of freedom fitted are much lower than the k values
you are specifying, so you could afford to reduce them (see ?choose.k )

  Remember the rule of thumb that you should not be trying to fit more
than *at most* N/10 parameters, where N is your number of points -- so
quadratic models of 3 independent predictors (= 7 parameters, intercept
+ 2 for each predictor variable) would already be overfitting slightly.

  cheers
    Ben Bolker

2011/5/18 Gavin Simpson<gavin.simpson at ucl.ac.uk>

On Wed, 2011-05-18 at 10:53 -0500, ARISTIDES LOPEZ wrote:

Dear members list,

I'm trying to make a model for descrive the distribution of demersal

fishes

in the Colombian Caribbean Sea. I have a data set of n= 56, the model is
like this: Density (ind/km2) ~ s(Lat) + s(Long) + s(deep). The problem

is

that R give me the error message *"Model has more coefficients than

data"*.

Anybody knows how can avoid this?

Faithfully.

Each of your smooths will be using k = 10 degrees of freedom so that is
30 degrees of freedom already, which is a lot for a data set of 56
observations.

Are all the data unique? i.e. you have 56 unique density values, 56
unique lats, 56 unique lons etc. If not, it might be the the unique
information in the data is not sufficient to support the complexity of
the smooths.

My money would be on that you did something you haven't actually told
us, and have more smooths in the model than you say and they are using
more degrees of freedom than it appears to us.

The easy way to try to solve the problem, will be to restrict the
complexity of the individual smooths:

response ~ s(Lat, k = 6) + s(Long, k = 6) + s(deep, k = 6)

for example.

You could probably model these data as a Possion with an offset term for
the km2 covered by each sample, rather than treating these as a density.

HTH,

G

--
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
  Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
  Pearson Building,             [e] gavin.simpsonATNOSPAMucl.ac.uk<http://gavin.simpsonatnospamucl.ac.uk/>
  Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%

-----BEGIN PGP SIGNATURE-----
Version: GnuPG v1.4.10 (GNU/Linux)
Comment: Using GnuPG with Mozilla - http://enigmail.mozdev.org/
-----END PGP SIGNATURE-----



------------------------------

Message: 11
Date: Thu, 19 May 2011 07:35:39 +0100
From: Gavin Simpson<gavin.simpson at ucl.ac.uk>
To: ARISTIDES LOPEZ<aristideslpz at gmail.com>
Cc: r-sig-ecology at r-project.org
Subject: Re: [R-sig-eco] Error message in GAM
Message-ID:<1305786939.2773.3.camel at chrysothemis.geog.ucl.ac.uk>
Content-Type: text/plain; charset="UTF-8"

On Wed, 2011-05-18 at 17:16 -0500, ARISTIDES LOPEZ wrote:

Dear Dr. Gavin,

Thank you very much for your help. All my data are unique (because I have

56

different stations). As you suggest I restrict the
complexity of the individual smooths:

response ~ s(Lat, k = 9) + s(Long, k = 9) + s(deep, k = 9)

Problem solved.

Now I try to make other model:

  modelo2<-gam(Density~s(year, k=6)+s(Month, k=6)+s(rainfall, k=6),
family=Gamma, data=at)

The "new" problem is that R give me the next error  *"Error en
smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) :
   A term has fewer unique covariate combinations than specified maximum
degrees of freedom"*.

It means exactly what it says. One of the terms in the model:

      * s(year, k = 6)
      * s(Month, k = 6)
      * s(rainfall, k = 6)

has *fewer* then 6 unique values. Look at the outputs from

with(at, table(year))
with(at, table(Month))
with(at, table(rainfall))

to see which it(they) is(are).

G

Anybody knows what mean this?

Regards.

2011/5/18 Gavin Simpson<gavin.simpson at ucl.ac.uk>

On Wed, 2011-05-18 at 10:53 -0500, ARISTIDES LOPEZ wrote:

Dear members list,

I'm trying to make a model for descrive the distribution of demersal

fishes

in the Colombian Caribbean Sea. I have a data set of n= 56, the model

is

like this: Density (ind/km2) ~ s(Lat) + s(Long) + s(deep). The problem

is

that R give me the error message *"Model has more coefficients than

data"*.

Anybody knows how can avoid this?

Faithfully.

Each of your smooths will be using k = 10 degrees of freedom so that is
30 degrees of freedom already, which is a lot for a data set of 56
observations.

Are all the data unique? i.e. you have 56 unique density values, 56
unique lats, 56 unique lons etc. If not, it might be the the unique
information in the data is not sufficient to support the complexity of
the smooths.

My money would be on that you did something you haven't actually told
us, and have more smooths in the model than you say and they are using
more degrees of freedom than it appears to us.

The easy way to try to solve the problem, will be to restrict the
complexity of the individual smooths:

response ~ s(Lat, k = 6) + s(Long, k = 6) + s(deep, k = 6)

for example.

You could probably model these data as a Possion with an offset term for
the km2 covered by each sample, rather than treating these as a density.

HTH,

G

--
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
  Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
  Pearson Building,             [e] gavin.simpsonATNOSPAMucl.ac.uk<http://gavin.simpsonatnospamucl.ac.uk/>
  Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%

--
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
  Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
  Pearson Building,             [e]
gavin.simpsonATNOSPAMucl.ac.uk<http://gavin.simpsonatnospamucl.ac.uk/>
  Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%

	[[alternative HTML version deleted]]