similar to: Help with using unpenalised te smooth in negative binomial mgcv gam

Hey all, I'm using the gam() function inside the mgcv package to fit a penalised spline to some data. However, I don't quite understand what exactly the intercept it includes by default is / how to interpret it. Ideally I'd like to understand what the intercept is in terms of the B-Spline and/or truncated power series basis representation. Thanks!

Anyone using the penalised partial likelihood routines in survival5 should update their version. A bug has been fixed in the S package: in coxph() models with penalised likelihood and strata it was possible in some circumstances to get an infinite loop or perhaps an incorrect answer. The new version (2.3) is on cran.r-project.org and will percolate through CRAN in the next few days. -thomas

Anyone using the penalised partial likelihood routines in survival5 should update their version. A bug has been fixed in the S package: in coxph() models with penalised likelihood and strata it was possible in some circumstances to get an infinite loop or perhaps an incorrect answer. The new version (2.3) is on cran.r-project.org and will percolate through CRAN in the next few days. -thomas

Hi R-users I am trying to use the GAM function of the mgcv package. But I am having problem trying to specify the k parameter. Although I managed to run some models by giving to the parameter some (random) value, and it is explained by Wood (2006) that it does not seem to "really" affect the final result, I would like to grasp better its meaning. I understand that is the

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)

Hi all, I would like to export the smooth function estimate I got from gam to plot it in another graphics software. In S-plus I use the function preplot() for that, but it seems not to work in R. Has somebody an idea how to solve that? Thanks Stephanie ******************************** Stephanie von Klot Institut f?r Epidemiologie GSF - Forschungszentrum f?r Umwelt und Gesundheit Ingolst?dter

Dear List, I'm getting an error in mgcv, and I can't figure out where it comes from. The setup is the following: I've got a fitted GAM object called "MI", and a vector of "prediction data" (with default values for predictors). I feed this into predict.gam(object, newdata = whatever) via the following function: makepred = function(varstochange,val){ for

mgcv: Constructing probabilities for binomial GAM with crossed random intercepts and factor by variable Hello, (I'm sorry if this has been discussed elsewhere; I may not have been looking in the right places.) I ran a binomial GAM in which "Correct" is modelled in terms of the participant's age and the modality in which the stimulus is presented (written vs spoken).

Dear all, for demonstration purposes I want to display the basis functions used by a thin plate regression spline in a gamm model. I've been searching the help files, but I can't really figure out how to get the plots of the basis functions. Anybody an idea? Some toy code : require(mgcv) require(nlme) x1 <- 1:1000 x2 <- runif(1000,10,500) fx1 <- -4*sin(x1/50) fx2 <-

Dear R-hep, I'm trying to get the first derivative of a smooth from a gam model like: model<-gam(y~s(x,bs="cr", k=5)+z) and need the derivative: ds(x)/dx. Since coef(model) give me all the parameters, including the parameters of the basis, I just need the 1st derivative of the basis s(x).1, s(x).2, s(x).3, s(x).4. If the basis were generated with the function

Hello All, I'm actually the system administrator of a UNIX system where several users use R version 2.6.0. I have a user who is trying to use the SURVEY package, and when he does, he gets the message: survey' is not a valid package -- installed < 2.0.0? When I run the library() command, I get (see below): Anything that is listed as ** No title available (pre-2.0.0 install?) **

Readers, Using version 251 I tried the following command: lm(y~a+b,data=datafile) Resulting in, inter alia: ... coefficients (intercept) a 1.2 3.4 Packages installed: acepack ace() and avas() for selecting regression transformations adlift An adaptive lifting scheme algorithm akima Interpolation of irregularly spaced

Good Afternoon Everyone, I am looking for advice fitting a linear mixed model where the random components do not seem to fit within the model formulae for lmer. The columns of Z are not stratified and have the notional random formula (z1 | 1) + ... + (zk | 1). Context I am fitting a penalised thin plate spline with knots k1 to kn. The basis functions Zk are |x-ki|^3 and the penalty matrix has

Hello, I have posted this mail a few days ago but I did it wrong, I hope is right now: I have the following doubts related with lm.ridge, from MASS package. To show the problem using the Longley example, I have the following doubts: First: I think coefficients from lm(Employed~.,data=longley) should be equal coefficients from lm.ridge(Employed~.,data=longley, lambda=0) why it does not happen?

When I type library() to see what is installed the following list in RED comes up. Packages in library '/home/jason/R/i686-pc-linux-gnu-library/2.13': abind Combine multi-dimensional arrays aplpack Another Plot PACKage: stem.leaf, bagplot, faces, spin3R, and some slider functions biglm bounded memory linear and

In the summary of the gam object produced by gamm, the "Approximate significance of smooth terms" appears to be a test of the improvement in fit over a linear model, rather than a test of the significance of the overall effect of x on y: test.gamm<-gamm(y~te(x, bs="cr"), random=list(grp=~1)) summary(test.gamm$gam) . . . Approximate significance of smooth terms:

** High Priority ** Hello R users My issue is both theorical and technical. I would like to run a penalised discriminant analysis with the fda() function, but I don''t know all the details of splines theory. I try on the example of the phonems from the article "Penalised Discriminant Analysis" of Hastie, Buja and Tibshirani 1994 : 5 groups and 256 variables. The 256

A nearly complete port of the new survival5 package has been sent to CRAN and will soon be appearing on a mirror near you in the contrib/devel area. This new package, the successor to survival4, has a more stable likelihood maximiser for parametric survival models and incorporates penalised likelihoods for adding smoothing splines, ridge regression, and (approximately) frailties to survival

Dear R help list, This is a long post so apologies in advance. I am estimating a model with the mgcv package, which has several covariates both linear and smooth terms. For 1 or 2 of these smooth terms, I "know" that the truth is monotonic and downward sloping. I am aware that a new package "scam" exists for this kind of thing, but I am in the unfortunate situation that I am

I am confused on how the log-likelihood is calculated in a parametric survival problem with frailty. I see a contradiction in the frailty() help file vs. the source code of frailty.gamma(), frailty.gaussian() and frailty.t(). The function frailty.gaussian() appears to calculate the penalty as the negative log-density of independent Gaussian variables, as one would expect: >