similar to: mgcv: increasing basis dimension

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)

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

Hi, I've been using gamm4 to build GAMMs for exploring environmental influences on genetic ancestry. Things have gone well and I have 2 very straightforward questions: 1. I've used method=REML. Am I correct that this is an alternative method for estimating the smooth functions in GAMMs rather than GCV that is often used for GAMs? I've read up on REML and it makes sense, but I'm

I am using gam in mgcv (1.3-22) and trying to use gcv to help with model selection. However, I'm a little confused by the process of assessing GCV scores based on their magnitude (or on relative changes in magnitude). Differences in GCV scores often seem "obvious" with my poisson gams but with negative binomial, the decision seems less clear. My data represent a similar pattern as

Hello, After reinstalling the whole OS and R as well, I tried to update.packages() and get the follwing error message: concerning the mgcv update: atlas2-base is installed and blas as well (on debian). I haven't found lf77blas, I assume it's a library or something similar associated with blas. any suggestion how to solve that, thanks Martin * Installing *source* package

Hi I just upgraded my debian/stable to R 2.7.1 via apt-get install r-base r-base-core r-base-dev, and then began to update.packages() > update.packages(lib.loc="/usr/local/lib/R/site-library") > update.packages(lib.loc="/usr/lib/R/library") but I get: .... * Installing *source* package 'mgcv' ... ** libs gcc -std=gnu99 -I/usr/share/R/include -fpic -g

I have 11 possible predictor variables and use them to model quite a few target variables. In search for a consistent manner and possibly non-manual manner to identify the significant predictor vars out of the eleven I thought the option "select=T" might do. Example: (here only 4 pedictors) first is vanilla with "select=F" >

Updating my 1.8.0 R installation (>update.packages() ) I obtain the following (SORRY FOR THE LENGTH OF THE LOG BUT IT HELPS!!!): ................ downloaded 135Kb KernSmooth : Version 2.22-11 in /usr/lib/R/library Version 2.22-12 on CRAN Update (y/N)? y mgcv : Version 0.9-3.1 in /usr/lib/R/library Version 0.9-6 on CRAN Update (y/N)? y trying URL

Greetings I run into a compilation error when updating to mgcv_1.1-7 in R 2.0.0 on OS X 10.3. Note that other pacakges have compiled nicely. Some details are given below, but in short it looks like it's seeking for /usr/local/lib/powerpc-apple-darwin6.8/3.4.2/ which I don't have. But I do have /usr/lib/gcc/darwin/3.3 i.e a lower version of GCC in a different directory. More

Greetings, I am learning R My machine has these; CPU: 3cores amd64 OS pure-64bit CBLFS liux compiled from sources (kernel 3.2.1, gcc-4.6.2 R-2.15 When I compiled R the compiler spewed out lines like these:- make[3]: Entering directory `/tmp/RtmpiHdDJy/R.INSTALL472339eeb23a/mgcv/src' gcc -m64 -std=gnu99 -I/home/Rman/R-2.15.0/include -DNDEBUG - I/usr/local/atlas/include

Hello, I have a question regarding model selection and dropping of terms for GAMs fitted with package mgcv. I am following the approach suggested in Wood (2001), Wood and Augustin (2002). I fitted a saturated model, and I find from the plots that for two of the covariates, 1. The confidence interval includes 0 almost everywhere 2. The degrees of freedom are NOT close to 1 3. The partial

hi, i'm using gam() function from package mgcv with default option (edf estimated by GCV). >G=gam(y ~ s(x0, k = 5) + s(x1) + s(x2, k = 3)) >SG=summary(G) Formula: y ~ +s(x0, k = 5) + s(x1) + s(x2, k = 3) Parametric coefficients: Estimate std. err. t ratio Pr(>|t|) (Intercept) 3.462e+07 1.965e+05 176.2 < 2.22e-16 Approximate significance of smooth

First of all, I have GAMs noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian (link=log),groupD,methods=REML) Then I type " summary(noxd)". and show Family: gaussian Link function: log Formula: newNO2 ~ pressure

Dear R-devel, I am experiencing issues with running GAM models using mclapply, it fails to return any values if the data input becomes large. For example here the code runs fine with a df of 100 rows, but fails at 1000. library(mgcv) library(parallel) > df <- data.frame( + x = 1:100, + y = 1:100 + ) > > mclapply(1:2, function(i, df) { + fit <- gam(y ~ s(x, bs =

Dear R experts, I'm hoping someone can help me to interpret the results of building gam's with mgcv in R. Below are summaries of two gam's based on the same dataset. The first gam (named "gam.mod") has six predictor variables. The second gam (named "gam.mod2") is exactly the same except it is missing one of the predictor variables. What is confusing me is

Hi, I need further help with my GAMs. Most models I test are very obviously non-linear. Yet, to be on the safe side, I report the significance of the smooth (default output of mgcv's summary.gam) and confirm it deviates significantly from linearity. I do the latter by fitting a second model where the same predictor is entered without the s(), and then use anova.gam to compare the

hello I'm learning mgcv and would like to obtain numerical output corresponding to plot.gam. I can do so when seWithMean=FALSE (the default) but only approximately when seWithMean=TRUE. Can anyone show how to obtain the exact values? Alternatively, can you clarify the explanation in the manual "Note that, if seWithMean=TRUE, the confidence bands include the uncertainty about the

I received an assignment that I have to do in R, but I'm absolutely not very good at it. The task is the following: http://www.nabble.com/file/p22804957/question8.jpg To do this, we also get the following pieces of code (not in correct order): http://www.nabble.com/file/p22804957/hints.jpg I'm terrible at this and I'm completely stuck. The model I chose can be found in here:

This is not mission critical, but it's bothering me. I'm getting inconsistent results when I use the $ accessor in the gam formula *In window #1:* > library(mgcv) > dat=data.frame(x=1:100,y=sin(1:100/50)+rnorm(100,0,.05)) > str(dat) > gam(dat$y~s(dat$x)) Error in eval(expr, envir, enclos) : object 'x' not found > *In window #2:* > gm = gam(dat$cf~s(dat$s)) >

hello to everybody: I would to know with ranges of GCV or UBRE values can be considered as adequate to consider a GAM as correct Thanks in advance -- David Nogu?s Bravo Functional Ecology and Biodiversity Department Pyrenean Institute of Ecology Spanish Research Council Av. Monta?ana 1005 Zaragoza - CP 50059 976716030 - 976716019 (fax)