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ge:
"Note that smooth ids are not supported for random effect terms. Unlike most smooth
terms, side conditions are never applied to random effect terms in the event of nesting
(since they are identifiable without side conditions)."
When I do a search on "using gam mgcv formu
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d provide commented, minimal, self-contained, reproducible code.
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d curves using predict(gamm1$gam,...,
type="terms") supplying the factor levels and correctedAges at which you
want to evaluate the curves.
> Many thanks!
> L
>
>
>
> On Thu, Apr 6, 2017 at 8:22 AM, Simon Wood <mailto:simon.w...@bath.edu>> wrote:
>
>
for (A)?
Is it the reference level? Or is it, perhaps, the "grand mean" of the shape
variable?
Thank you in advance for your time,
Fotis
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s(ctrial, sbj,
bs="re") , data=data_a, family=binomial)
Error in G$smooth[[i]]$first.para:G$smooth[[i]]$last.para :
argument of length 0
Any idea on what that error might be?
Thank you in advance for your time.
Fotis
P.S.: R version: 3.3.1, mgcv version: 1.8.15
--
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;re") + s(ctrial, sbj,
bs="re") , data=data_a, family=binomial)
Error in G$smooth[[i]]$first.para:G$smooth[[i]]$last.para :
argument of length 0
Any idea on what that error might be?
Thank you in advance for your time.
Fotis
P.S.: R version: 3.3.1, mgcv version: 1.8.15
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m/ilapros/DoubleRobGam
Best
Ilaria
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Dear Øystein,
In your code 'id' is set up to be numeric. In your first gamm call it
gets coerced to a factor (since nothing else would make sense). In your
second gamm call it is simply treated as numeric, since that could makes
sense. To make the two models equivalent you just need to substi
This first derivative penalty spline will do it, but the price paid is
that the curves are often quite wiggly.
library(mgcv); set.seed(5)
x <- runif(100); y <- x^4 + rnorm(100)*.1
b <- gam(y~s(x,m=1))
pd <- data.frame(x=seq(-.5,1.5,length=200))
ff <- predict(b,pd,se=TRUE)
plot(x,y,xlim=c(-
I would quote the p-value, but not the statistic (as it is not a
standard F stat). The actual statistic is given here:
https://academic.oup.com/biomet/article-pdf/100/4/1005/566200/ast038.pdf
On 14/12/2018 04:33, Smith, Desmond wrote:
Dear All,
I have a mgcv::gam model of the form:
m1 <- gam
ge, could you
> pinpoint us to where the problem may occur? Is it something within the C-code
> used within the package (as the last error seems to indicate), or is it
> related to the computer cluster?
>
> Any help or insights is much appreciated.
>
> Kind regards,
>
> Frank
>
> [[alternative HTML version deleted
SN. On p-values for smooth components of an extended generalized
additive model. Biometrika 2013;100(1):221-228
or thereafter, but I am not a statistician. I could use help understanding
what the reference degrees of freedom are.
Thank you,
Susan
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Thanks,
Andrew
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oops, I just realised that the fix referred to below is in mgcv 1.8-3 -
not yet on CRAN.
On 25/08/14 13:20, Simon Wood wrote:
Hi Andrew,
In some of the shots you sent then top was reporting several cores
working. I think the problem here may be to do with the way bam is
parallelized - at
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Cetáceos - UFRRJ/ IF/ DCA
2014-10-15 15:22 GMT-04:00 Simon Wood mailto:s.w...@bath.ac.uk>>:
Can you give the result of typing
sessionInfo()
in the session where this happens, please?
On 15/10/14 16:48, Rodrigo Tardin wrote:
Hi all,
I am not sure if this is
,
Erin Conlisk
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iated. Thank you very much.
Best
Diego
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___
27;d probably use te(x1,x2) unless x1 and x2 are really on the same
scale, in which case s(x1,x2) might be appropriate. The `by' variable
trick is probably not going to work so well for interactions, however
(it's not so clear what the by variable should be).
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(x2,by=x2) since interactions are surely present and
> i'm not sure if a linear combination is enough.
>
> Thanks!
> Dominik
>
>
> On Wed, May 11, 2016 at 3:11 AM, Simon Wood <mailto:simon.w...@bath.edu>> wrote:
>
> The spline hav
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mailman/listinfo/r-help
> thz.ch/mailman/listinfo/r-help>
> stat.ethz.ch
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>>> PLEASE do read the
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a$Year),FUN=mean) names(Fit_factor1)=c("Bin","Fitted")
> > Fit_factor1
>
> ��� Bin� Fitted
> 1� 1997 0.017094017
> 2� 1998 0.010652463
> 3� 1999 0.02300
> 4� 2000 0.017167382
> 5� 2001 0.030465950
> 6� 2002 0.007446809
> 7� 2003 0.0105680
straints automatically, but if this sort of functional
ANOVA is a major component of what you want to do, then it is probably worth
checking out the gss package and Chong Gu's book on smoothing spline ANOVA.
best,
Simon
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t; > strictly nested in te(x1, x2), eg.
> >
> > y ~ s(x1, bs = "cr", k = 10) + s(x2, bs = "cr", k = 10)
> >
> > is strictly nested within
> >
> > y ~ te(x1, x2, k = 10)
> > ## is the same as y ~ te(x1, x2, bs = "cr", k = 10
oject.org/posting-guide.html and provide commented, minimal,
> self-contained, reproducible code.
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_with_climate)
>
> Error in if (length(data) != vl) { :
> missing value where TRUE/FALSE needed
> Calls: gam ... smooth.construct -> smooth.construct.tp.smooth.spec -> array
> In addition: Warning message:
> In array(0, n * k) : NAs introduced by coercion
> Execution
,PC2,PC3,PC6,PC7,PC8) .
>
> I have renamed PC1.1,PC2.1,PC3.1,PC4.1,PC5.1 to PC6,PC7,PC8,PC9,PC10 for
> simplicity.
>
> Regards
>
> On Tuesday 01 September 2009 17:31:04 Simon Wood wrote:
> > The basic problem is that you have requested a 10 dimensional thin plate
>
like to extract the partial predictions for e.g. "s(b.depth)", given
> the inclusion of the other variables.
>
> --
> Staffan Roos, PhD
> Research Ecologist
> BTO Scotland
>
> __
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> I am using gam in gam package (not in mgcv) it is possible to force
> gam in mgcv to behave like gam in gam package?
-- not *exactly*, no. But what do you want to do? (i.e. what feature of `gam'
do you need?)
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> Simon Wood, Mathematical Sciences, University of Bath, Ba
r choice of knots and dimension? Thank you very much.
>
> Lee
>
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> +44 1225 386603 www.maths.bat
. You get an F-statistic for
> > each s() term by default and you are referred to saummary.gam for further
> > explanation.
> >
> > David Winsemius, MD
> > Heritage Laboratories
> > West Hartford, CT
>
> [[alternative HTML version deleted]]
>
>
; Thanks!!!
>
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this?
> Kind regards,
> Daniel Rabczenko
>
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gt; https://stat.ethz.ch/mailman/listinfo/r-help
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> self-contained, reproducible code.
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> +44 1225 386
elp
> PLEASE do read the posting guide
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> +44 1225 386603
th the package {mgcv}.
> >
> > My command is: gam(y~s(x1,bs="cr")+s(x2, bs="cr")).
> >
> > I need help to know what are the default basis funcitons for gam. I have
> > not found any detailed reference for this.
> >
> > Ca
an be
> found in here:
> myknots6 = c(20,24,34)
> p.spline8 = spm(accel ~
> f(times,basis="trunc.poly",degree=2,knots=myknots6))
>
> Can someone please help me with this task? I'm kind of desperate.
>
> Thank you!
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> Sim
601.27
> 2 1201.43848 601.290.77246-0.02
>
> Thank you very much!
>
> Jianghua Liu, University of Sheffield
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> Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK
> +44 1225 386603 www.mat
ces in DF(0.77246) and deviance (-0.02), these two models
>
> seem to be not significantly different. Isn't it?
> Thank you anyway!
>
> Simon Wood-4 wrote:
> > The simpler model has the lower deviance (marginally), so there is
> > nothing to
> > test here. This can
between
> two smooth terms, together with the interaction of this interaction with
> factor. Is model 2 reasonable? I find it is rather complicated to interpret
> the plot of model 2.
> Thank you very much for helping!
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> Simon Wood, Mathematical Sciences, University of Bath, Bat
you might need to increase the value supplied in `sp' in order to get the EDF
for the smooth term close enough to 1 for your liking.
best,
Simon
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> +44 1225 386603 www.maths.bath.ac.uk/~sw283
AM and
> GAMM using AIC legitimate? Of course I will do additional model
> plotting using residuals etc. as well but it seems important to me to
> have a more direct method of comparing these models (I�m aware of the
> fact that AIC is a rough estimate when it comes to generalized mixed
>
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d, minimal, self-contained, reproducible code.
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= 1 n = 5107
```
ACF functions:
[4]: https://i.stack.imgur.com/7nbXS.png
[5]: https://i.stack.imgur.com/pNnZU.png
Data can be found on my [GitHub][6] site in the file
[data_cross_validated_post2.rds][7]. A csv version is also available.
This is my code:
```r
library(readr)
library(m
On 21/07/2022 15:19, jade.shodan--- via R-help wrote:
Hello everyone (incl. Simon Wood?),
I'm not sure that my original question (see below, including
reproducible example) was as clear as it could have been. To clarify,
what I would to like to get is:
1) a perspective plot of temperat
ented, minimal, self-contained, reproducible code.
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and in particular I am getting the message
Error in initial.sp(w * x, S, off) : S[[1]] matrix is not +ve definite.
After reading everything I could find on mrf, it sounds like there was a bug that was brought up with Simon Wood in 2012, due to differences between windows and
sion.string R version 3.0.1 (2013-05-16)
nickname Good Sport
package mgcv version 1.7-22
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I think it's going to be a problem to have different sized groups in
your second model. ?corSymm says that a general correlation matrix is
being estimated (i.e. the correlation between each pair of observations
is being estimated - for this to be meaningful across groups you need
the jth price
Probably you didn't want to set x0=0:1? Here is some code, to do what you want.
(The CI shape is not identical to the plot(b) version as the uncertainty
includes
the uncertainty in the other smooths and the intercept now.)
library(mgcv)
set.seed(2)
dat <- gamSim(1,n=400,dist="normal",scale=2)
b
I can't see anything immediately wrong except:
1. presumably there are repeated values in 'road_quiet' aren't there? If
so then your inequality constraint matrix
will contain constraints that are *exact* copies of each other. I'm not
sure, and don't immediately have time to try it
out, but it c
Hi Alice,
This is a bug in gam.side, which was assuming that any interaction
smooth that needed identifiability constraints would be penalized.
thanks for reporting it - fixed for next version.
In the meantime have you considered using `ti' terms instead of `te'?
These provide a more satisfa
ther = sin(z)
fit = gam(y~s(x)+s(z)+other)
plot(fit,all.terms=TRUE)
plot(fit,select=3,xlab="???")
dev.copy2pdf(file = "This.pdf.needs.proper.labels.pdf", height = 8,
width = 8)
Any ideas for workarounds?
Many thanks as always,
Andrew
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knots,soc.imp=soc.knots,sand.imp=sand.knots,clay.imp=clay.knots,
abslat.imp=abslat.knots
)
,method = "REML"
,weights = 1/nsame
), error=function(e) e, finally=doyee)
if(inherits(r.ints, "error")) {r.ints=doyee; print("an error hap
Andrew,
I think that the weird edfs may result from an unhandled case in the
side constraint calculation. In particular the term
s(BCAR.imp,bs="cr",k=length(BCAR.knots),by=as.factor(pot.trial))
is confounded with
te(soc.imp,BCAR.imp,k=c(4,4))
but there was an issue with picking this up prop
osting guide http://www.R-project.org/posting-guide.html
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quot; formula specification work if it
doesn't work "as intended" by for example lm?
Many thanks,
Andrew
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ble.com.
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ailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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+44 (0)1225 386603 http://people.bath.ac.uk/sw28
,
family=Tweedie(p=1.6,link=log),data=df,method="REML")
gives a model that is closer to being reasonable (p-values are then
consistent between select=TRUE and FALSE).
best,
Simon
On 18/04/13 14:24, Simon Wood wrote:
Jan,
Thanks for this. Is there any chance that you could send me th
a gam object are called up?
My harder question: how might one construct principled analogs of these
statistics in an MI context, when degrees of freedom will vary across
models, depending on the imputed data? Has anybody thought about this,
or do I have have some serious pencil-and-paper
I would be very nervous about relying on an anova call here. It will
attempt a generalized likelihood ratio test, but gamm is using penalized
quasi likelihood and there is really no likelihood here (even without
the problem that if there was a likelihood the null hypothesis would
still be on th
help mailing list archive at Nabble.com.
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le parameter estimate or a signal of fixed scale
parameter, to be passed to testStat.
> 2) what is testStat?
mgcv:::testStat
- probably worth looking at the original source code which includes some
comments. It does the actual computation.
best,
Simon
> thanks
>
> Greg
>
>
Well it's not pretty, but I guess you could try something like this...
best,
simon
library(mgcv)
dat <- gamSim(1,n=200,dist="normal",scale=2)
b <- gam(y ~ s(x1)+s(x2),data=dat)
m <- 40
x1 <- seq(0,1,length=m)
x2 <- seq(0,1,length=m)
pd <- expand.grid(x1=x1,x2=x2)
fv <- predict(b,newdata=pd,se=TRU
which makes this work?
f<-function(t) (Dpq/(2*t) + F3q + Mp/2*log(2*pi*t) - m3$gcv)^2
optimise(f,interval=c(0.5,1.5))$min->phix
Dpq/(2*phix) + F3q + Mp/2*log(2*pi*phix)
m3$gcv
#but what is phix - not the Pearson estimate or the scale returned by gam?
thanks
Greg Dropkin
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ly I missunderstanding some key elements in gam modelling or using
incorrect syntaxis. I don't know what the problem is. Any ideas will be
helpful.
Sergio
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On 16/10/12 07:32, Andrew Crane-Droesch wrote:
Hi All,
I'm running into a problem with GAM (in the MGCV package). When I try
to estimate the model, I get the following error message:
1> fit <-
gam(ndvi~s(rain)+s(temp)+s(rainl1)+s(rainl2)+s(rainxY)+s(rainl1xY)+s(rainl2xY)+s(tempxY),
Hi Andrew,
Could you send me a bit more information (off list, as this is likely to
get into obscure details), please?
In particular can you let me know the mgcv and R version numbers, the
server operating system and, if possible, what BLAS it has installed?
best,
Simon
On 16/10/12 07:32, An
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
+44 (0)1225 386603 http://people.bath.ac.uk/sw283
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I'm looking for well-commented versions of various functions comprising
mgcv,
Commented versions are available e.g. in mgcv_*.*-**.tar.gz from CRAN
(unzip and look in mgcv/R). Well commented versions is another matter...
best,
Simon
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Simon Wood, Mathematical Science, University of Bat
))
[1] TRUE
Any suggestions where to start?
Mark
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-co
b$xy.idx)
nb$E <- factor(sprintf(id.fmt,nb$x.idx+1,nb$y.idx),levels=nb$xy.idx)
nb$W <- factor(sprintf(id.fmt,nb$x.idx-1,nb$y.idx),levels=nb$xy.idx)
nb.mat <- sapply(nb[,c("N","S","E","W")],as.numeric)
nb.l <- lapply(split(nb.mat,nb$xy.idx),functio
th[[1]]$knots,
coef(my_gam)[-1], mm)
plot(my_gam)
lines(x,spline_y) # why is this different? It seems shifted on the x-
and y-axis
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PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and prov
__
R-help@r-project.org mailing list
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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.
--
Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
+44
g/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
+44 (0)1225 386603 http://people.bath.ac.uk/sw283
__
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R-help@r-project.org mailing list
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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.
--
Simon Wood, Mathematical Science, University
k of
convergence warnings but then the model saying it was not converged and
vice versa. The QP seemed to fit the data well (and quickly) so we
switched to that.
Thanks again.
Trevor
On Wed, Jul 16, 2014 at 12:44 PM, Simon Wood mailto:s.w...@bath.ac.uk>> wrote:
Trevor,
It looks li
ad the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
+44 (0)1225 386603 http://people.bath.ac.uk/sw283
__
ide.html
and provide commented, minimal, self-contained, reproducible code.
--
Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
+44 (0)1225 386603 http://people.bath.ac.uk/sw283
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Hi Silje,
Thanks for this. I guess RUTE is a numeric variable in this model and
hence only has one associated random coefficient? This then causes a
problem when calling predict.gam as part of processing 'residuals=T'.
I've fixed the problem for the next release, but did you really want a
sin
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