similar to: 0.64.1 alpha floating point exception in make check

The following used to work (0.62.1 the latest that I used), and now it doesn't (0.63.0): force <- vector(mode = "list", length = 1) names(force)[1] <- somename It returns the error: Error in "[<-"(NULL, 1) : object is not subsetable You might argue that this approach is unnecessary now that R allows subscripts that are out of range (e.g., force<-list();

Hello again! Thanks to Dr. Brian Ripley, the make problem I reported earlier has been solved, and R-0.64.1 is now successfully installed and tested. There were two essential problems with the stock configuration: (i) the Fortran option -fPIC was not being propagated to certain Makefiles further down the tree; (ii) gmake was not being recognized when the library files were being

Greetings, I'm attempting to install R-0.64.1 on a Sun Sparc 20 running Solaris 2.4 with gcc-2.7.2.2, g77-0.5.20 and Gnu make 3.76-1. On several occasions, the make process aborted when trying to build the shared object file for the modreg package (its first attempt at such a beast). In all attempts, the error message is as follows: ../../../../bin/R SHLIB -o modreg.so bsplvd.o bvalue.o

Dear R users, I am looking for the source code of the implementation of ppr (Projection Pursuit Regression) in R. It will be great if citations of the source papers on which the implementation is based, are also provided. Thank you, Arvind Iyer, Grad student, Deptt. of Biomedical Engineering Viterbi School of Engineering University of Southern California, Los Angeles [[alternative HTML

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 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

Hi, I have a 4 column file (long/lat/elev/variable) and I tried to fit the values of my variable to the XYZ space using Tps and I keep getting the following message: Warning messages: 1: GCV search gives a minumum at the endpoints of the grid search in: Krig.find.gcvmin(info, lambda.grid, gcv.grid$GCV, Krig.fgcv, 2: GCV search gives a minumum at the endpoints of the grid search in:

Readers, Using version 251 I tried the following command: lm(y~a+b,data=datafile) Resulting in, inter alia: ... coefficients (intercept) a 1.2 3.4 Packages installed: acepack ace() and avas() for selecting regression transformations adlift An adaptive lifting scheme algorithm akima Interpolation of irregularly spaced

Is there an R implementation of a scheme for automatic smoothing parameter selection with loess, e.g., by minimizing one of the AIC/GCV statistics discussed by Hurvich, Simonoff & Tsai (1998)? Below is a function that calculates the relevant values of AICC, AICC1 and GCV--- I think, because I to guess from the names of the components returned in a loess object. I guess I could use

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'm seeing some strange behavior while using the snow package for networked computers. I believe it's caused by name space resolution issues, and would appreciate any suggestions tracking it down. First, is there a way to find out what frame (as in frames in environments, not data frames) a name is being obtained from or put into? Second, how closely does the evaluation environment in

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)

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 all, I have run a ridge regression as follows: reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+final$u, lambda=seq(0,10,0.01)) Then I enter : select(reg) and it returns: modified HKB estimator is 19.3409 modified L-W estimator is 36.18617 smallest value of GCV at 10 I think it means that it is advisable to

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

Hello, I was trying to compile R-base-0.64.1 on Linux/Alpha (RedHat 6.0). It compiled but it couldn't pass the make check (choked with tests/Examples/modreg-Ex.R). I used gdb, and it turned out that the src/library/modreg/src/sinerp.f was the problem. else if(j.eq.nk)then c1 = 0d0 c2 = 0d0 c3 = 0d0 endif > p1ip(1,j) = 0d0-

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

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 =

Hi has anyone succeeded in installing R on DEC alpha OSF 4.0 with gcc/g77 ? The PLATFORMS file mentions successful installtion on OSF 3.2 using cc. Here is the output I get when trying to compile: ld -shared -o eda.so line.o smooth.o ld: Warning: Unresolved: floor ceil rsort __exc_add_pc_range_table __exc_add_gp_range __exc_remove_pc_range_table __exc_remove_gp_range make[4]: Leaving

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