Displaying 20 results from an estimated 20000 matches similar to: "`power' documentation slip (PR#8838)"
2005 Nov 28
1
terms.object documentation bug? (PR#8353)
Full_Name: simon wood
Version: 2.2.0 (and lower)
OS: linux/windows
Submission from: (NULL) (86.135.153.59)
I think that the documentation for the `specials' attribute of a `terms.object'
is not quite right:
specials: If the 'specials' argument was given to 'terms.formula' there
is a 'specials' attribute, a list of vectors indicating the
terms
2006 Dec 04
1
GAM model selection and dropping terms based on GCV
Hello,
I have a question regarding model selection and dropping of terms for GAMs fitted with package mgcv. I am following the approach suggested in Wood (2001), Wood and Augustin (2002).
I fitted a saturated model, and I find from the plots that for two of the covariates,
1. The confidence interval includes 0 almost everywhere
2. The degrees of freedom are NOT close to 1
3. The partial
2012 May 23
1
mgcv: How to calculate a confidence interval of a ratio
Dear R-Users,
Dr. Wood replied to a similar topic before where confidence intervals were
for a ratio of two treatments (
https://stat.ethz.ch/pipermail/r-help/2011-June/282190.html). But my
question is more complicated than that one. In my case, log(E(y)) = s(x)
where y is a smooth function of x. What I want is the confidence interval
of a ratio of log[(E(y2))/E(y1)] given two fixed x values of
2006 Dec 15
1
DF for GAM function (mgcv package)
For summary(GAM) in the mgcv package smooth the degrees of freedom for
the F value for test of smooth terms are the rank of covariance matrix
of \hat{beta} and the residuals df. I've noticed that in a lot of GAMs
I've fit the rank of the covariance turns out to be 9. In Simon Wood's
book, the rank of covariance matrix is usually either 9 or 99 (pages
239-230 and 259).
Can anyone
2007 Dec 26
1
Cubic splines in package "mgcv"
R-users
E-mail: r-help@r-project.org
My understanding is that package "mgcv" is based on
"Generalized Additive Models: An Introduction with R (by Simon N. Wood)".
On the page 126 of this book, eq(3.4) looks a quartic equation with respect
to
"x", not a cubic equation. I am wondering if all routines which uses
cubic splines in mgcv are based on this quartic
2007 Jun 21
1
mgcv: lowest estimated degrees of freedom
Dear list,
I do apologize if these are basic questions. I am fitting some GAM
models using the mgcv package and following the model selection criteria
proposed by Wood and Augustin (2002, Ecol. Model. 157, p. 157-177). One
criterion to decide if a term should be dropped from a model is if the
estimated degrees of freedom (EDF) for the term are close to their lower
limit.
What would be the
2007 Oct 03
1
How to avoid overfitting in gam(mgcv)
Dear listers,
I'm using gam(from mgcv) for semi-parametric regression on small and
noisy datasets(10 to 200
observations), and facing a problem of overfitting.
According to the book(Simon N. Wood / Generalized Additive Models: An
Introduction with R), it is
suggested to avoid overfitting by inflating the effective degrees of
freedom in GCV evaluation with
increased "gamma"
2006 Apr 11
1
gaussian family change suggestion
Hi,
Currently the `gaussian' family's initialization code signals an error if
any response data are zero or negative and a log link is used. Given that
zero or negative response data are perfectly legitimate under the GLM
fitted using `gaussian("log")', this seems a bit unsatisfactory. Might
it be worth changing it?
The current offending code from `gaussian' is:
2011 Jan 14
1
naresid.exclude query
x <- NA
na.act <- na.action(na.exclude(x))
y <- rep(0,0)
naresid(na.act,y)
... currently produces the result...
numeric(0)
... whereas the documentation might lead you to expect
NA
The behaviour is caused by the line
if (length(x) == 0L) return(x)
in `stats:::naresid.exclude'. Removing this line results in the behaviour I'd
expected in the above example (and in a
2007 Oct 02
3
mcv package gamm function Error in chol(XVX + S)
Hi all R users !
I'm using gamm function from Simon Wood's mgcv package, to fit a spatial
regression Generalized Additive Mixed Model, as covariates I have the
geographical longitude and latitude locations of indexed data. I include a
random effect for each district (dist) so the code is
fit <- gamm(y~s(lon,lat,bs="tp", m=2)+offset(log(exp.)),
random=list(dist=~1),
2010 Jun 16
3
mgcv, testing gamm vs lme, which degrees of freedom?
Dear all,
I am using the "mgcv" package by Simon Wood to estimate an additive mixed
model in which I assume normal distribution for the residuals. I would
like to test this model vs a standard parametric mixed model, such as the
ones which are possible to estimate with "lme".
Since the smoothing splines can be written as random effects, is it
correct to use an (approximate)
2009 Feb 07
1
paraPen in gam [mgcv 1.4-1.1] and centering constraints
Dear Mr. Simon Wood, dear list members,
I am trying to fit a similar model with gam from mgcv compared to what I
did with BayesX, and have discovered the relatively new possibility of
incorporating user-defined matrices for quadratic penalties on
parametric terms using the "paraPen" argument. This was really a very
good idea!
However, I would like to constraint the coefficients
2006 Jul 03
1
gamm
Hello,
I am a bit confused about gamm in mgcv. Consulting Wood (2006) or Ruppert et al. (2003) hasn't taken away my confusion.
In this code from the gamm help file:
b2<-gamm(y~s(x0)+s(x1)+s(x2)+s(x3),family=poisson,random=list(fac=~1))
Am I correct in assuming that we have a random intercept here....but that the amount of smoothing is also changing per level of the
2013 Jun 17
1
Can you use two offsets in gam (mgcv)?
Hello,
I have been trying to find out whether it is possible to use more than one
offset in a gam (in mgcv).
The reason I would like to do this is to 1) account for area surveyed in a
Poisson model of sightings of porpoises within defined grid cells (each cell
has a slightly different area) and 2) account for detection probability
within each grid cell (some grid cells are further away from the
2008 May 06
1
mgcv::gam shrinkage of smooths
In Dr. Wood's book on GAM, he suggests in section 4.1.6 that it might be
useful to shrink a single smooth by adding S=S+epsilon*I to the penalty
matrix S. The context was the need to be able to shrink the term to zero if
appropriate. I'd like to do this in order to shrink the coefficients towards
zero (irrespective of the penalty for "wiggliness") - but not necessarily
all the
2011 Jun 21
5
please help for mgcv package
i read a book from WOOD, there's an example which is talking about the
pollutant.
library(gamair)
library(mgcv)
y<-gam(death~s(time,bs="cr",k=200)+s(pm10median,bs="cr")+s(so2median,bs="cr")+s(o3median,bs="cr")+s(tmpd,bs="cr"),data=chicago,family=Possion)
lag.sum<-function(a,10,11)
{n<-length(a)
b<-rep(0,n-11)
for(i in 0:(11-10))
2012 Jul 14
1
GAM Chi-Square Difference Test
We are using GAM in mgcv (Wood), relatively new users, and wonder if anyone
can advise us on a problem we are encountering as we analyze many short time
series datasets. For each dataset, we have four models, each with intercept,
predictor x (trend), z (treatment), and int (interaction between x and z).
Our models are
Model 1: gama1.1 <- gam(y~x+z+int, family=quasipoisson) ##no smooths
Model
2011 Apr 19
1
Prediction interval with GAM?
Hello,
Is it possible to estimate prediction interval using GAM? I looked through
?gam, ?predict.gam etc and the mgcv.pdf Simon Wood. I found it can
calculate confidence interval but not clear if I can get it to calculate
prediction interval. I read "Inference for GAMs is difficult and somewhat
contentious." in Kuhnert and Venable An Introduction to R, and wondering why
and if that
2018 Jan 12
1
setting constraints on gam
Thanks Simon, by cloning a smooth construct do you mean copying and
modifying the smooth constructor code? Could you pleas elaborate on
your answer? Which is the Predict.matrix method?
2018-01-12 3:20 GMT-06:00 Simon Wood <simon.wood at bath.edu>:
> There probably is a way, but it involves some programming. You would need to
> clone a smooth constructor (e.g. for the "cr"
2013 Mar 11
1
Use pcls in "mgcv" package to achieve constrained cubic spline
Hello everyone,
Dr. wood told me that I can adapting his example to force cubic spline to pass through certain point.
I still have no idea how to achieve this. Suppose we want to force the cubic spline to pass (1,1), how can
I achieve this by adapting the following code?
# Penalized example: monotonic penalized regression spline .....
# Generate data from a monotonic truth.