similar to: unable to compile mgcv

Hope I am not sounding too much like a broken record - as far as 64-bit build on AIX is concerned. ** Short - 32-bit builds complete normally, 64-bit builds stop at "installing 'sysdata.rda'" with message: Error: Line starting 'Package: tools ...' is malformed! Execution halted *** Longer... I could file a bug - Importance, showstopper - but I would prefer to better

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

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

I have been looking further and need some help with: tools:::sysdata2LazyLoadDB("/data/prj/cran/R-3.2.3/src/library/tools/R/sysdata.rda","../../../library/tools/R") Error: Line starting 'Package: tools ...' is malformed! Details: root at x065:[/data/prj/cran/64/R-3.2.3/src/library/tools]export | egrep "PATH|HOME" HOME=/

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

On 2015-12-18 02:29, Simon Urbanek wrote: > Michael, > > I got access to PDP AIX so I can try to replicate your problem. Can you, please, share exactly your setup - AIX version and well as how exactly you installed the compilers (=where from)? I can then try to replicate it. AFAICS there is no official binary for gfortran nor gcc 4.7 so it must be some 3rd party - which could also be a

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

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

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

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

Hi,everyone, I am studying the generalized additive model and employ the package 'mgcv' developed by professor Wood. However,I can not understand the example listed in check.in function. For example, library(mgcv) set.seed(1) dat <- gamSim(1,n=400,scale=2) ## fit a GAM with quite low `k' b<-gam(y~s(x0,k=6)+s(x1,k=6)+s(x2,k=6)+s(x3,k=6),data=dat) plot(b,pages=1,residuals=TRUE)

Hello All, I'm getting this error (Version: 1.9.0-1 on a debian system) > update.packages("mgcv") trying URL `ftp://mirror.aarnet.edu.au/pub/cran/src/contrib/PACKAGES' ftp data connection made, file length 169516 bytes opened URL .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... ..........

Dear all, I'm trying to fit a pure additive model of the following formula : fit <- gam(y~x1+te(x2, x3, bs="cr")) ,with the smoothing parameter estimation method "magic"(default). Regarding this, I have two questions : Question 1 : In some cases the value of "mgcv.conv$fully.converged" becomes "FALSE", which tells me that the method stopped with a

Hi, I am fairly new to GAM and started using package mgcv. I like the fact that optimal smoothing is automatically used (i.e. df are not determined a priori but calculated by the gam procedure). But the mgcv manual warns that p-level for the smooth can be underestimated when df are estimated by the model. Most of the time my p-levels are so small that even doubling them would not result

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

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" >

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"

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 All: I tried to update the package mgcv from my current version 1.1-8 to mgcv version 1.2. Using the command update.packages("mgcv") to update mgcv failed in my computer and the returning message was: "Error in old.packages(lib.loc=lib.loc, contriburl=contriburl, method=method, :no installed.packages for (?invalid) lib.loc=mgcv" When I typed installed.packages(), I got

Dear friends, I have install the last version 2.10 and now I have probles with 'mgcv' which can be load. I proved to eliminated and reinstalled but always obtain the same message: > library (mgcv) This is mgcv 1.6-1. For overview type `help("mgcv-package")'. Error in runif(1) : .Random.seed is not an integer vector but of type 'list' Error : .onAttach non