Displaying 20 results from an estimated 9000 matches similar to: "multidimensional function fitting"
2003 Feb 28
0
[despammed] RE: multidimensional function fitting
You can use R objects, such as the return from gam, and the
predict.gam function, from C. See the R extensions manual.
Reid Huntsinger
-----Original Message-----
From: RenE J.V. Bertin [mailto:rjvbertin at despammed.com]
Sent: Thursday, February 27, 2003 3:42 PM
To: Wiener, Matthew
Cc: r-help at stat.math.ethz.ch
Subject: Re: [despammed] RE: [R] multidimensional function fitting
On Thu, 27
2003 Feb 27
0
[despammed] RE: multidimensional function fitting
If something like the second-order function does not fit your data well, it
may well be that the data do not admit a simple structure that you can
easily code in C.
If you expect the structure of the function to be simple, tell gam() so by
specifying a small dimensional basis (via the k= argument in s()). This
will probably ease the computational burden.
HTH,
Andy
> -----Original
2006 Feb 27
1
clustering
Hi there,
Sorry for the double email. Does R have the packages for the following
clustering methods? And if it does, what the commands for them?
1. SOM (Self-organization map)
2. Graph partitioning:
3. Neural network
4. Probability Binning
Thank you very much!
Linda
[[alternative HTML version deleted]]
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
2004 Jun 14
2
CVnn2 + nnet question
Hi,
I am trying to determine the number of units in the hidden layer
and the decay rate using the CVnn2 script found in MASS directory
(reference: pg 348,MASS-4).
The model that I am using is in the form of Y ~ X1 + X2 + X3...
+ X11 and the underlying data is time-series in nature.
I found the MASS and nnet package extremely useful (many thanks
to the contributors).
However I am getting
2011 Dec 09
3
gam, what is the function(s)
Hello,
I'd like to understand 'what' is predicting the response for library(mgcv)
gam?
For example:
library(mgcv)
fit <- gam(y~s(x),data=as.data.frame(l_yx),family=binomial)
xx <- seq(min(l_yx[,2]),max(l_yx[,2]),len=101)
plot(xx,predict(fit,data.frame(x=xx),type="response"),type="l")
I want to see the generalized function(s) used to predict the response
2009 May 27
3
Neural Network resource
Hi All,
I am trying to learn Neural Networks. I found that R has packages which can help build Neural Nets - the popular one being AMORE package. Is there any book / resource available which guides us in this subject using the AMORE package?
Any help will be much appreciated.
Thanks,
Indrajit
2003 Jun 03
1
S+ style implementation of GAM for R?
Hi,
I've got the R library "mgcv" for GAM written by Simon Wood which works well
in many instances. However, over the years I
got attached to the S+ implementation of GAM which allows loess smoothing in
more than 1 dimension as well as spline smoothing.
Has anyone ported the S+ GAM library to R?
Regards,
Doug Beare.
Fisheries Research Services,
Marine Laboratory,
Victoria Road,
2011 Aug 26
1
methods() not listing some S3 plot methods...?
Dear List,
This may be related to this email thread initiated by Ben Bolker last
month: https://stat.ethz.ch/pipermail/r-devel/2011-July/061630.html
In answering this Question on StackOverflow
http://stackoverflow.com/q/7195628/429846 I noticed that `methods()` was
not listing some S3 methods for `plot()` provided by the mgcv package.
At the time I wanted to check the development version of R as
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
2005 Oct 05
3
testing non-linear component in mgcv:gam
Hi,
I need further help with my GAMs. Most models I test are very
obviously non-linear. Yet, to be on the safe side, I report the
significance of the smooth (default output of mgcv's summary.gam) and
confirm it deviates significantly from linearity.
I do the latter by fitting a second model where the same predictor is
entered without the s(), and then use anova.gam to compare the
2013 Nov 06
3
Nonnormal Residuals and GAMs
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 is optimal over all unbiased
estimators iff the data meet the conditions linearity,
2007 Jun 22
1
two basic question regarding model selection in GAM
Qusetion #1
*********
Model selection in GAM can be done by using:
1. step.gam {gam} : A directional stepwise search
2. gam {mgcv} : Smoothness estimation using GCV or UBRE/AIC criterion
Suppose my model starts with a additive model (linear part + spline part).
Using gam() {mgcv} i got estimated degrees of freedom(edf) for the smoothing
splines. Now I want to use the functional form of my model
2010 Dec 14
2
Use generalised additive model to plot curve
Readers,
I have been reading 'the r book' by Crawley and think that the
generalised additive model is appropriate for this problem. The
package 'gam' was installed using the command (as root)
install.package("gam")
...
library(gam)
> library(gam)
Loading required package: splines
Loading required package: akima
> library(mgcv)
This is mgcv 1.3-25
Attaching
2007 Oct 05
2
question about predict.gam
I'm fitting a Poisson gam model, say
model<-gam(a65tm~as.factor(day.week
)+as.factor(week)+offset(log(pop65))+s(time,k=10,bs="cr",fx=FALSE,by=NA,m=1),sp=c(
0.001),data=dati1,family=poisson)
Currently I've difficulties in obtaining right predictions by using
gam.predict function with MGCV package in R version 2.2.1 (see below my
syntax).
2010 Jun 27
1
mgcv out of memory
Hello, I am trying to update the mgcv package on my Linux box and I keep
getting an "Out of memory!" error. Does anyone know of a fix for this?
Below is a snippet of the message that I keep getting: Thank you. Geoff
** R
** inst
** preparing package for lazy loading
** help
*** installing help indices
>>> Building/Updating help pages for package 'mgcv'
Formats:
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
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
2002 Sep 15
7
loess crash
Hi,
I have a data frame with 6563 observations. I can run a regression with
loess using four explanatory variables. If I add a fifth, R crashes. There
are no missings in the data, and if I run a regression with any four of the
five explanatory variables, it works. Its only when I go from four to five
that it crashes.
This leads me to believe that it is not an obvious problem with the data,
2009 Feb 18
1
Training nnet in two ways, trying to understand the performance difference - with (i hope!) commented, minimal, self-contained, reproducible code
Dear all,
Objective: I am trying to learn about neural networks. I want to see
if i can train an artificial neural network model to discriminate
between spam and nonspam emails.
Problem: I created my own model (example 1 below) and got an error of
about 7.7%. I created the same model using the Rattle package (example
2 below, based on rattles log script) and got a much better error of
about