similar to: mgcv 1.2-0

mgcv 1.0 (package providing gams etc) will be released with R 1.9.0. (R 1.8.x compatible versions can be found at: http://www.stats.gla.ac.uk/~simon/simon/mgcv.html) There are quite a few changes from mgcv 0.9: hence this message. The main new features are: * A generalized additive mixed modelling function `gamm' (which uses lme from the nlme library of glmmPQL from the MASS library for

mgcv 1.0 (package providing gams etc) will be released with R 1.9.0. (R 1.8.x compatible versions can be found at: http://www.stats.gla.ac.uk/~simon/simon/mgcv.html) There are quite a few changes from mgcv 0.9: hence this message. The main new features are: * A generalized additive mixed modelling function `gamm' (which uses lme from the nlme library of glmmPQL from the MASS library for

Hi, I think there is a problem with the LAPACK version of qr() in version 1.7.0. (version below). 1. The documentation states that LAPACK=TRUE is the default, but the code has LAPACK=FALSE. 2. With LAPACK=TRUE qr() is never pivoting, even in cases where it very clearly should be. e.g. set.seed(0) X<-matrix(rnorm(40),10,4);X[,1]<-X[,2] qrx<-qr(X,LAPACK=TRUE) qrx$pivot # note, no

Hi! I'm learning mgcv, and reading Simon Wood's book on GAMs, as recommended to me earlier by some folks on this list. I've run into a question to which I can't find the answer in his book, so I'm hoping somebody here knows. My outcome variable is binary, so I'm doing a binomial fit with gam(). I have five independent variables, all continuous, all uniformly

Hola! I have some problems with gam in mgcv. Firts a detail: it would be nice igf gam would accept an na.action argument, but that not the main point. I want to have a smooth term for time over a year, the same pattern repeating in succesive years. It would be natural then to impose the condition s(0)=s(12). Is this possible within mgcv? I tried to obtain this with trigonometric terms, aca:

The rpanel package builds on the tcltk package to provide simple interactive controls for R functions, in particular to provide simple forms of dynamic graphics. The intention is to make this form of control particularly easy for R users to implement, with full documentation. The necessary tcltk variables are managed behind the scenes so that users need not be concerned with any

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

Hi all! I?ve got some problems with the function gam (library mgcv). For some models I get the error message : Error: no valid set of coefficients has been found:please supply starting values In addition: Warning message: NaNs produced in: log(x) This is a shortened code I used: gam(y ~ M1 + M3 + M4 + M5 + M6 + sex + M1*M3 + s(age), family=Gamma(link ="identity"), weights=days) If

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:

Hi everyone, I ran a binomial GAM consisting of a tensor product of two continuous variables, a continuous parametric term and crossed random intercepts on a data set with 13,042 rows. When trying to plot the tensor product with vis.gam(), I get the following error message: Error in persp.default(m1, m2, z, col = col, zlim = c(min.z, max.z), xlab = view[1], : invalid 'z' limits In

I am trying to test out several mgcv::gam models in a scalar-on-function regression analysis. The following is the 'hierarchy' of models I would like to test: (1) Y_i = a + integral[ X_i(t)*Beta(t) dt ] (2) Y_i = a + integral[ F{X_i(t)}*Beta(t) dt ] (3) Y_i = a + integral[ F{X_i(t),t} dt ] equivalents for discrete data might be: 1) Y_i = a + sum_t[ L_t * X_it * Beta_t ] (2) Y_i

In R version 2.7.0 (2008-04-22) as.numeric(".") returns zero. > as.numeric(".") [1] 0 This must be a bug. Splus and previous versions of R (<= 2.6.0) return NA, as you might expect. I'm running R version 2.7.0 (2008-04-22) on Windows XP. Paul _____________________________________________________ Paul Johnson Robertson Centre for Biostatistics University of

Hi list, I'm working with gamm models of this sort, using Simon Wood's mgcv library: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group in order to test whether tensor product smooths vary across factor levels. I was wondering if comparing those two

Dear All, I am trying to produce log-log plots in R and I was wondering if any of you have a 'template' for generating these with 'nice' labels and log-log grids? I know I can set up axes individually and use the intervals I want, however, I will be producing a large number of these plots and would not like to do this manually for each of them + I am very new to R and at the

hi, i'm working with mgcv packages and specially gam. My exemple is: >test<-gam(B~s(pred1)+s(pred2)) >plot(test,pages=1) when ploting test, you can view pred1 vs s(pred1, edf[1] ) & pred2 vs s(pred2, edf[2] ) I would like to know if there is a way to access to those terms (s(pred1) & s(pred2)). Does someone know how? the purpose is to access to equation of smooths terms

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

here is a distillation of a problem encountered in transfering some working code from R-0.63 to R-1.1.0 a1 <- 1:10 b1 <- tapply(a1,a1, sum) c1 <- ts(b1) c1 Error in if (NCOL(x) == 1) { : missing value where logical needed note that the error is returned as the value of calling ts() and is not automatically displayed problem seems to be that is.array(b1) returns TRUE yet dim(b1)

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.

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,

mgcv 1.4 is now on CRAN. It includes new features to allow mgcv::gam to fit almost any (quadratically) penalized GLM, plus some extra smoother classes. New gam features ------------------------- * Linear functionals of smooths can be included in the gam linear predictor, allowing, e.g., functional generalized linear models/signal regression, smooths of interval data, etc. * The parametric