Displaying 20 results from an estimated 9000 matches similar to: "gam --- a new contributed package"
2005 Apr 06
0
Version 0.93 of GAM package on CRAN
I have posted an update to the GAM package. Note that this package
implements gam() as described
in the "White" S book (Statistical models in S). In particular, you can
fit models with lo() terms (local regression)
and/or s() terms (smoothing splines), mixed in, of course, with any
terms appropriate for glms.
A number of bugs in version 0.92 have been fixed; notably
1) some problems
2005 Apr 06
0
Version 0.93 of GAM package on CRAN
I have posted an update to the GAM package. Note that this package
implements gam() as described
in the "White" S book (Statistical models in S). In particular, you can
fit models with lo() terms (local regression)
and/or s() terms (smoothing splines), mixed in, of course, with any
terms appropriate for glms.
A number of bugs in version 0.92 have been fixed; notably
1) some problems
2007 Dec 18
1
R-users
R-users
E-mail: r-help@r-project.org
I have a quenstion on "gam()" in "gam" package.
The help of gam() says:
'gam' uses the _backfitting
algorithm_ to combine different smoothing or fitting methods.
On the other hand, lm.wfit(), which is a routine of gam.fit() contains:
z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *
2007 Dec 18
2
"gam()" in "gam" package
R-users
E-mail: r-help@r-project.org
I have a quenstion on "gam()" in "gam" package.
The help of gam() says:
'gam' uses the _backfitting
algorithm_ to combine different smoothing or fitting methods.
On the other hand, lm.wfit(), which is a routine of gam.fit() contains:
z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *
2003 Sep 16
2
gam and concurvity
Hello,
in the paper "Avoiding the effects of concurvity in GAM's .." of Figueiras et
al. (2003) it is mentioned that in GLM collinearity is taken into account in
the calc of se but not in GAM (-> results in confidence interval too narrow,
p-value understated, GAM S-Plus version). I haven't found any references to
GAM and concurvity or collinearity on the R page. And I
2006 Jun 18
1
GAM selection error msgs (mgcv & gam packages)
Hi all,
My question concerns 2 error messages; one in the gam package and one in
the mgcv package (see below). I have read help files and Chambers and
Hastie book but am failing to understand how I can solve this problem.
Could you please tell me what I must adjust so that the command does not
generate error message?
I am trying to achieve model selection for a GAM which is required for
2002 Jan 28
6
Almost a GAM?
Hello:
I sent this question the other day with the wrong subject
heading and couple typos, with no response. So,
here I go again, having made those corrections.
I would like to estimate, for lack of a better description,
a partially additive non-parametric model with the following
structure:
z~ f(x,y):w1 + g(x,y):w2 + e
In other words, I'd like to estimate the marginals with
respect to
2007 Feb 27
1
interactions and GAM
Dear R-users,
I have 1 remark and 1 question on the inclusion of interactions in the gam function from the gam package.
I need to fit quantitative predictors in interactions with factors. You can see an example of what I need in fig 9.13 p265 from Hastie and Tibshirani book (1990).
It's clearly stated that in ?gam "Interactions with nonparametric smooth terms are not fully
2003 Sep 14
3
Re: Logistic Regression
Christoph Lehman had problems with seperated data in two-class logistic regression.
One useful little trick is to penalize the logistic regression using a quadratic penalty on the coefficients.
I am sure there are functions in the R contributed libraries to do this; otherwise it is easy to achieve via IRLS
using ridge regressions. Then even though the data are separated, the penalized
2007 Dec 12
0
Hep on using GAM() in R
Dear friends,
I met some problem on using GAM() function in R.
Any help or suggestions are greatly appreciated.
# My programs and problems are list below#
library(splines)
library(gam)
point<-read.csv("d:/gam.csv",sep=",",header=TRUE) #read the data
gam.opt<-gam(mark~lo(x,y,span=0.2)+lo(lstday2004,span=0.2)+lo(slope,span=0.2)+lo(ndvi2004,span=
2006 Jul 28
1
could someone help me to install packages "gam" (ubuntu 6.06)
> install.packages("gam")
Warning in install.packages("gam") : argument 'lib' is missing: using
/usr/local/lib/R/site-library
--- Please select a CRAN mirror for use in this session ---
Loading Tcl/Tk interface ... done
trying URL 'http://cran.cnr.Berkeley.edu/src/contrib/gam_0.97.tar.gz'
Content type 'application/x-gzip' length 89613 bytes
opened URL
2005 Oct 12
0
step.gam- question
This is covered in the helpfile, but perhaps not clearly enough.
The gam chapter in the "white book" has more details.
step.gam moves around the terms in the scope aregumnet in an ordered
fashion.
So if a scope element is
~ 1 + x +s(x,4) + s(x,8)
and the formula at some stage is ~ x + ....
then if direction="both", the routine checks both "1" and
2008 Feb 28
0
use of step.gam (from package 'gam') and superassignment inside functions
Hello,
I am using the function step.gam() from the 'gam' package (header info
from library(help=gam) included below) and have come across some
behavior that I cannot understand. In short, I have written a function
that 1) creates a dataframe, 2) calls gam() to create a gam object, then
3) calls step.gam() to run stepwise selection on the output from gam().
When I do this, gam()
2004 Dec 01
2
step.gam
Dear R-users:
Im trying (using gam package) to develop a stepwise analysis. My gam
object contains five pedictor variables (a,b,c,d,e,f). I define the
step.gam:
step.gam(gamobject, scope=list("a"= ~s(a,4), "b"= ~s(b,4), "c"= ~s(c,4),
"d"= ~s(d,4), "e"= ~s(e,4), "f"= ~s(f,4)))
However, the result shows a formula containing the whole
2006 Jan 19
2
gam
Dear R users,
I'm new to both R and to this list and would like to get
advice on how to build generalized additive models in R.
Based on the description of gam, which I found on the R
website, I specified the following model:
model1<-gam(ST~s(MOWST1),family=binomial,data=strikes.S),
in which ST is my binary response variable and MOWST1 is a
categorical independent variable.
I get the
2010 Jan 26
1
AIC for comparing GLM(M) with (GAM(M)
Hello
I'm analyzing a dichotomous dependent variable (dv) with more than 100
measurements (within-subjects variable: hours24) per subject and more
than 100 subjects. The high number of measurements allows me to model
more complex temporal trends.
I would like to compare different models using GLM, GLMM, GAM and
GAMM, basically do demonstrate the added value of GAMs/GAMMs relative
to
2002 Nov 13
2
Comparing GAM objects using ANOVA
Hi,
Is it possible to compare two GAM objects created with the gam() function from the mgcv package. I use a slightly modified version of anova.glm() named anova.gam(), modified from John Fox (2002). It often gives me some aberant responses, especially with "F" test. I use a quasibinomial model and scale (dispersion) is calculated and used in the calculation of the F value. Does someone
2003 Jun 04
2
gam()
Dear all,
I've now spent a couple of days trying to learn R and, in particular, the
gam() function, and I now have a few questions and reflections regarding
the latter. Maybe these things are implemented in some way that I'm not yet
aware of or have perhaps been decided by the R community to not be what's
wanted. Of course, my lack of complete theoretical understanding of what
2005 Oct 12
1
step.gam and number of tested smooth functions
Hi,
I'm working with step.gam in gam package. I'm interested both in spline and
lowess functions and when I define all the models that I'm interested in I get
something like that:
> gam.object.ALC<-gam(X143S~ALC,data=dane,family=binomial)
>
2005 Sep 26
4
p-level in packages mgcv and gam
Hi,
I am fairly new to GAM and started using package mgcv. I like the
fact that optimal smoothing is automatically used (i.e. df are not
determined a priori but calculated by the gam procedure).
But the mgcv manual warns that p-level for the smooth can be
underestimated when df are estimated by the model. Most of the time
my p-levels are so small that even doubling them would not result