similar to: mgcv 1.5-0

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

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

The plots look reasonable to me. The plot of residuals against linear predictor always looks scary when many of the fitted values are very close to zero, so I tend to look at residuals against sqrt(fitted) in such cases. I don't think that the presence of the zero curve is a reason to reject the model --- it's easy to produce such plots by fitting a completely correct model to simulated

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

Hi Greg, For quasi families I've used extended quasi-likelihood (see Mccullagh and Nelder, Generalized Linear Models 2nd ed, section 9.6) in place of the likelihood/quasi-likelihood in the expression for the (RE)ML score. I hadn't realised that this was possible before the paper was published. best, Simon ps. sorry for slow reply, the original message slipped through my filter for

re: Cubic splines in package "mgcv" I don't have access to Gu (2002) but clearly the function R(x,z) defined on p126 of Simon Wood's book is piecewise quartic, not piecewise cubic. Like Kunio Takezawa (below) I was puzzled by the word "cubic" on p126. As Simon Wood writes, this basis is not actually used by mgcv when specifying bs="cr". Maybe the point is

Hi, According to the help file for model.frame/get_all_vars, the following should produce the same output from both functions, but it doesn't... > dat <- list(X=matrix(1:15,5,3),z=26:30) > model.frame(~z+X,dat) z X.1 X.2 X.3 1 26 1 6 11 2 27 2 7 12 3 28 3 8 13 4 29 4 9 14 5 30 5 10 15 > get_all_vars(~z+X,dat) [1] z X <NA> <NA> <0

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:

Dear list, I fitted the same GAM model using directly the function gam(mgcv) ... then as a parameter of another function that capture the warnings messages (see below). In the first case, there is no warning message printed, but in the last one, the function find two warning messages stating "matrix not positive definite" So my question is: Do I have to worry about those warnings and

Dear users, I am trying to show the equation (including coefficients from the model estimates) for a gam model but do not understand how to. Slide 7 from one of the authors presentations (gam-theory.pdf URL: http://people.bath.ac.uk/sw283/mgcv/) shows a general equation log{E(yi )} = ?+ ?xi + f (zi ) . What I would like to do is put my model coefficients and present the equation used. I am an

Hello all, I have count data with about 36% of observations being zeros. I found in some of the examples of the r-help mail archives that a tweedie family of distributions could be used to fit a model with random effects. Upon installing the tweedie package and attempting to fit the following model: lmer(SUS ~ 1 + (1|

hi Using a ts or tprs basis, I expected gcv to decrease when increasing the basis dimension, as I thought this would minimise gcv over a larger subspace. But gcv increased. Here's an example. thanks for any comments. greg #simulate some data set.seed(0) x1<-runif(500) x2<-rnorm(500) x3<-rpois(500,3) d<-runif(500) linp<--1+x1+0.5*x2+0.3*exp(-2*d)*sin(10*d)*x3

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,

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

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

Hi all, My question concerns 2 error messages; one in the gam package and one in the mgcv package (see below). I have read help files and Chambers and Hastie book but am failing to understand how I can solve this problem. Could you please tell me what I must adjust so that the command does not generate error message? I am trying to achieve model selection for a GAM which is required for

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"

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

Qusetion #1 ********* Model selection in GAM can be done by using: 1. step.gam {gam} : A directional stepwise search 2. gam {mgcv} : Smoothness estimation using GCV or UBRE/AIC criterion Suppose my model starts with a additive model (linear part + spline part). Using gam() {mgcv} i got estimated degrees of freedom(edf) for the smoothing splines. Now I want to use the functional form of my model

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