similar to: qgam

This would be easier if base::set.seed() accepted a value of .Random.seed instead of just a scalar integer or, new to R-3.0.0, NULL. If set.seed() returned the previous value of .Random.seed (NULL if there was no previous value) things might be even easier. People should not have to know where .Random.seed is stored. S+'s set.seed() accepts a value of .Random.seed but does not return the

Re: Heteroscedasticity and mgcv. (Collin Lynch) The GAMLSS package can model heterogeneity in the scale parameter (e.g. standard deviastion) [and also heterogeity in skewness and kurtosis parameters].of the response variable distribution. For parametric models a generalized likelihood ratio test can be used to test whether the heterogeity is needed. Alternatively a generalized Akaike

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 community, I encountered a very disturbing phenomenon today: When I try to fit any gam() with mgcv R aborts. I could not find any post regarding this in google, which mades in even more strange. I am using the latest Ubuntu, latest R and latest mgcv everything up to date. The crash occured too with the last mgcv 1.7-11. This is the output from the R shell: <pre> R version 2.14.0

Hello! Can anyone give a tip how to plot parametric effects in an Generalized Additive Model, from mgcv package? Thanks, PM

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

Dear all, I am using the "mgcv" package by Simon Wood to estimate an additive mixed model in which I assume normal distribution for the residuals. I would like to test this model vs a standard parametric mixed model, such as the ones which are possible to estimate with "lme". Since the smoothing splines can be written as random effects, is it correct to use an (approximate)

Hi, I have a set of measurements that are made on a daily basis over many years. I would like to produce a *non-parametric* smooth of these data to estimate the seasonal cycle - to achieve this, I have been using the cyclic cubic splines from the mgcv package. This works superbly in most situations, but not all. The problem is that for various practical reasons the data is not available all year

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,

Hi. I'm analysing some fish biological traits with a gam in mgcv. After several tries, I got this model log(tle) = sexcolor + s(doy, bs = "cc", by = sexcolor) +log(tl) sexcolor is a factor with 4 levels doy is "day of year", which is modeled as a smoother tl is "total length of the fish" The summary of this models is (only parametric coefficientes): Parametric

Readers, I have been reading 'the r book' by Crawley and think that the generalised additive model is appropriate for this problem. The package 'gam' was installed using the command (as root) install.package("gam") ... library(gam) > library(gam) Loading required package: splines Loading required package: akima > library(mgcv) This is mgcv 1.3-25 Attaching

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

mgcv version 1.2 is on CRAN now. mgcv provides generalized additive models and generalized additive mixed models with automatic estimation of the smoothness of model components. Changes in this version are: * A new gam fitting method is implemented for the generalized case. It provides more reliable convergence than the previous default, but can be a little slower. See ?gam.method,

mgcv version 1.2 is on CRAN now. mgcv provides generalized additive models and generalized additive mixed models with automatic estimation of the smoothness of model components. Changes in this version are: * A new gam fitting method is implemented for the generalized case. It provides more reliable convergence than the previous default, but can be a little slower. See ?gam.method,

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

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

R-users E-mail: r-help@r-project.org My understanding is that package "mgcv" is based on "Generalized Additive Models: An Introduction with R (by Simon N. Wood)". On the page 126 of this book, eq(3.4) looks a quartic equation with respect to "x", not a cubic equation. I am wondering if all routines which uses cubic splines in mgcv are based on this quartic

Do you have the mgcv package installed (I think it's part of the standard distro, though) /loaded? ziP is there, not in BAM. Other than that, sorry, no clue. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Mon, Nov 6,

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as