Displaying 20 results from an estimated 5000 matches similar to: "mgcv::predict.gam lpmatrix for prediction outside of R"
2008 Apr 06
0
mgcv::gam prediction using lpmatrix
The documentation for predict.gam in library mgcv gives an example of using
an "lpmatrix" to do approximate prediction via interpolation. However, the
code is specific to the example wrt the number of smooth terms, df's for
each,etc. (which is entirely appropriate for an example)
Has anyone generalized this to directly generate code from a gam object (eg
SAS or C code)? I wanted to
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
2008 Oct 01
1
Simon Wood GAMsetup
Dear Simon, Thank you for your quick reply!
I used to perform the GAMsetup in the following manner:
GAMsetup sintax:
x.summer: vector used for construct the spline
knots<-14
N<-length(x.summer)
x<-array(x.summer,dim=c(1,N))
G<-list(m=1,n=N,nsdf=0,df=knots+1,dim=1,s.type=0,by=0,by.exists=FALSE,p.order=0,x=x,n.knots=knots,fit.method="mgcv")
H<-GAMsetup(G)
with the
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
2008 Jun 11
1
mgcv::gam error message for predict.gam
Sometimes, for specific models, I get this error from predict.gam in library
mgcv:
Error in complete.cases(object) : negative length vectors are not allowed
Here's an example:
model.calibrate <-
gam(meansalesw ~ s(tscore,bs="cs",k=4),
data=toplot,
weights=weight,
gam.method="perf.magic")
> test <- predict(model.calibrate,newdata)
Error in
2005 Feb 27
1
prediction, gam, mgcv
I fitted a GAM model with Poisson distribution
using the function gam() in the mgcv package.
My model is of the form:
mod<-gam(y~s(x0)+s(x1)+s(x2),family=poisson).
To extract estimates at a specified set of covariate
values I used the gam `predict' method.
But I want to get
estimate and standard error of the difference of two fitted values.
Can someone explain what should I do?
Thank
2012 Sep 25
1
REML - quasipoisson
hi
I'm puzzled as to the relation between the REML score computed by gam and
the formula (4) on p.4 here:
http://opus.bath.ac.uk/22707/1/Wood_JRSSB_2011_73_1_3.pdf
I'm ok with this for poisson, or for quasipoisson when phi=1.
However, when phi differs from 1, I'm stuck.
#simulate some data
library(mgcv)
set.seed(1)
x1<-runif(500)
x2<-rnorm(500)
2007 Mar 27
1
gam parameter predictions --Sorry for double posting
R-help,
Sorry for posting again the same question (dated 26-03-2007) but
all my mails have been sent to the recycle bin without possibility
of recovering and thus I don't know if anyone has answer my query.
Here is the original message:
I'm applying a gam model (package mgcv) to predict
relative abundances of a fish species.
The covariates are year, month, vessel and statistical
2013 Mar 23
1
Time trends with GAM
Hi all,
I am using GAM to model time trends in a logistic regression. Yet I would
like to extract the the fitted spline from it to add it to another model,
that cannot be fitted in GAM or GAMM.
Thus I have 2 questions:
1) How can I fit a smoother over time so that I force one knot to be at a
particular location while letting the model to find the other knots?
2) how can I extract the matrix
2011 Mar 28
2
mgcv gam predict problem
Hello
I'm using function gam from package mgcv to fit splines. ?When I try
to make a prediction slightly beyond the original 'x' range, I get
this error:
> A = runif(50,1,149)
> B = sqrt(A) + rnorm(50)
> range(A)
[1] 3.289136 145.342961
>
>
> fit1 = gam(B ~ s(A, bs="ps"), outer.ok=TRUE)
> predict(fit1, newdata=data.frame(A=149.9), outer.ok=TRUE)
Error
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
2007 Dec 13
1
Two repeated warnings when runing gam(mgcv) to analyze my dataset?
Dear all,
I run the GAMs (generalized additive models) in gam(mgcv) using the
following codes.
m.gam
<-gam(mark~s(x)+s(y)+s(lstday2004)+s(ndvi2004)+s(slope)+s(elevation)+disbinary,family=binomial(logit),data=point)
And two repeated warnings appeared.
Warnings$B!'(B
1: In gam.fit(G, family = G$family, control = control, gamma = gamma, ... :
Algorithm did not converge
2: In gam.fit(G,
2007 Jun 25
1
gam function in the mgcv library
I would like to fit a logistic regression using a smothing spline, where the spline is a piecewise cubic polynomial. Is the knots option used to define the subintervals for each piece of the cubic spline? If yes and there are k knots, then why does the coefficients field in the returned object from gam only list k coefficients? Shouldn't there be 4k -4 coefficients?
Sincerely,
Bill
2010 Dec 06
1
Help with GAM (mgcv)
Please help! Im trying to run a GAM:
model3=gam(data2$Symptoms~as.factor(data2$txerad)+s(data2$maritalStatus),family=binomial,data=data2)
But keep getting this error:
Error in dl[[i]] : subscript out of bounds
Can someone please tell me what this error is?
Thanks
--
View this message in context: http://r.789695.n4.nabble.com/Help-with-GAM-mgcv-tp3074165p3074165.html
Sent from the R help
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"
2011 Feb 16
1
retrieving partial residuals of gam fit (mgcv)
Dear list,
does anybody know whether there is a way to easily retrieve the so called "partial residuals" of a gam fit with package mgcv? The partial residuals are the residuals you would get if you would "leave out" a particular predictor and are the dots in the plots created by
plot(gam.object,residuals=TRUE)
residuals.gam() gives me whole model residuals and
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
2010 Aug 04
2
more questions on gam/gamm(mgcv)...
Hi R-users,
I'm using R 2.11.1, mgcv 1.6-2 to fit a generalized additive mixed model.
I'm new to this package...and just got more and more problems...
1. Can I include correlation and/or random effect into gam( ) also? or only
gamm( ) could be used?
2. I want to estimate the smoothing function s(x) under each level of
treatment. i.e. different s(x) in each level of treatment. shall I
2007 Oct 04
1
Convergence problem in gam(mgcv)
Dear all,
I'm trying to fit a pure additive model of the following formula :
fit <- gam(y~x1+te(x2, x3, bs="cr"))
,with the smoothing parameter estimation method "magic"(default).
Regarding this, I have two questions :
Question 1 :
In some cases the value of "mgcv.conv$fully.converged" becomes
"FALSE", which tells me that the method stopped with a
2007 Dec 13
1
Probelms on using gam(mgcv)
Dear all,
Following the help from gam(mgcv) help page, i tried to analyze my
dataset with all the default arguments. Unfortunately, it can't be run
successfully. I list the errors below.
#m.gam<-gam(mark~s(x,y)+s(lstday2004)+s(slope)+s(ndvi2004)+s(elevation)+s(disbinary),family=binomial(logit),data=point)