similar to: glmnet_1.6 fails to install due to unsupported Fortran 90 compiler

Hi, is there any chance to install glmnet with gcc-4.5.2? For me it fails on all systems with: trying URL 'http://mirrors.softliste.de/cran/src/contrib/glmnet_1.7.tar.gz' Content type 'application/x-gzip' length 522888 bytes (510 Kb) opened URL ================================================== downloaded 510 Kb * installing *source* package ?glmnet? ... This package has only

We have submitted glmnet_1.6 to CRAN This version has an improved convergence criterion, and it also uses a variable screening algorithm that dramatically reduces the time to convergence (while still producing the exact solutions). The speedups in some cases are by a factors of 20 to 50, depending on the particular problem and loss function. See our paper

We have submitted glmnet_1.6 to CRAN This version has an improved convergence criterion, and it also uses a variable screening algorithm that dramatically reduces the time to convergence (while still producing the exact solutions). The speedups in some cases are by a factors of 20 to 50, depending on the particular problem and loss function. See our paper

Hi, I was trying to install package glmnet in R, but failed and it show such messages: * Installing *source* package glmnet ... This package has only been tested with gfortran. So some checks are needed. R_HOME is /home/username/R/R-2.9.0 Attempting to determine R_ARCH... R_ARCH is Attempting to detect how R was configured for Fortran 90.... Unsupported Fortran 90 compiler or Fortran 90

Hello, R version 2.8.0 (2008-10-20) on a linux-64-bit machine. I am trying to install the "glmnet" package and I get the following error: ******************************************************************************* > install.packages(pkgs = "glmnet") trying URL 'http://cran.parentinginformed.com/src/contrib/glmnet_1.1-5.tar.gz' Content type

https://bugs.freedesktop.org/show_bug.cgi?id=53402 Bug #: 53402 Summary: nouveau_bo.c:463:3: error: implicit declaration of function 'ttm_agp_tt_create' Classification: Unclassified Product: xorg Version: git Platform: x86-64 (AMD64) OS/Version: Linux (All) Status: NEW Severity:

Hi, > What compiler are you using to build with? I've made it default to clang with less looking around for llvm-gcc, so there may be an issue there. What is your configure line? What host are you trying to build on? First I have compiled llvm/clang compiled with "gcc (Gentoo 4.5.2 p1.1, pie-0.4.5) 4.5.2". Then I have installed llvm/clang. They are in the path: $ clang

On Sep 29, 2011, at 12:53 AM, Speziale Ettore wrote: > Hi, > >> What compiler are you using to build with? I've made it default to clang with less looking around for llvm-gcc, so there may be an issue there. What is your configure line? What host are you trying to build on? > > First I have compiled llvm/clang compiled with "gcc (Gentoo 4.5.2 p1.1, > pie-0.4.5)

On Sun, Jul 3, 2011 at 11:32 PM, Karel Gardas <karel.gardas at centrum.cz> wrote: > Hello, > > I asked here for kind of reference GCC version which LLVM development > team is using for *native* testing on ARM hardware. (no cross > compilation!) last week or so. I've been curious myself how the > situation looks and so I tested LLVM 2.9 as a reference point and LLVM >

update.packages(checkBuilt = TRUE) fails with: > update.packages(checkBuilt = TRUE) rpanel : Version 1.0-6 installed in /usr/lib64/R/library built under R 2.15.0 Version 1.1-1 available at http://mirrors.softliste.de/cran Update (y/N/c)? y trying URL 'http://mirrors.softliste.de/cran/src/contrib/rpanel_1.1-1.tar.gz' Content type 'application/x-gzip' length 702508 bytes (686

On 9/29/11 11:23 AM, Eric Christopher wrote: > On Sep 29, 2011, at 12:53 AM, Speziale Ettore wrote: > >> Hi, >> >>> What compiler are you using to build with? I've made it default to clang with less looking around for llvm-gcc, so there may be an issue there. What is your configure line? What host are you trying to build on? >> First I have compiled llvm/clang

Hi, I am attempting to evaluate the prediction error of a coxph model that was built after feature selection with glmnet. In the preprocessing stage I used na.omit (dataset) to remove NAs. I reconstructed all my factor variables into binary variables with dummies (using model.matrix) I then used glmnet lasso to fit a cox model and select the best performing features. Then I fit a coxph model

This is a new version of glmnet, that incorporates some bug fixes and speedups. * a new convergence criterion which which offers 10x or more speedups for saturated fits (mainly effects logistic, Poisson and Cox) * one can now predict directly from a cv.object - see the help files for cv.glmnet and predict.cv.glmnet * other new methods are deviance() for "glmnet" and coef() for

Hi all. Sorry for the long email. I have been trying to find someone local to work on this with me, without much luck. I went in to our local stats consulting service here, and the guy there told me that I already know more about model selection than he does. :-< He pointed me towards another professor that can perhaps help, but that prof is busy until mid-June, so I want to get as much

Dear R-helpers, Christoph (author of phyloclim) and Luke (author of tkrplot), I would like to get your helps on installing of phyloclim in Ubuntu linux. It seems a package named 'tkrplot' could not be installed at firstly, then packages depends on it could not be installed latter. As I have tested, installation of phyloclim works smoothly in Mac. I attempted to install these packages in

Hello I'm having trouble using lars and glmnet functions to predict on a new data set with different nrow then the original : for instance: ============= log.1 = glm(temp.data$TL~(.),temp.data,family = binomial,x=TRUE,y=TRUE) nrow(test.data) != nrow(temp.data # == TRUE Val.frame = model.frame(log.1,test.data) # returns a data frame with the variables needed to use log.1

glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models with "elastic net" regularization (tunable mixture of L1 and L2 penalties). glmnet uses pathwise coordinate descent, and is very fast. Some of the features of glmnet: * by default it computes the path at 100 uniformly spaced (on the log scale) values of the

glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models with "elastic net" regularization (tunable mixture of L1 and L2 penalties). glmnet uses pathwise coordinate descent, and is very fast. Some of the features of glmnet: * by default it computes the path at 100 uniformly spaced (on the log scale) values of the

Dear All, ? I have found differences between glmnet versions 1.7 and 1.7.1 which, in my opinion, are not cosmetic and do not appear in the ChangeLog. If I am not mistaken, glmnet appears to return different number of selected input variables, i.e. nonzeroCoef(fit$beta[[1]]) differes between versions. The code below is the same for 1.7.1 and 1.7, but you can see that outputs differ. I would

Hello fellow R users, I have been getting a strange error message when using the cv.glmnet function in the glmnet package. I am attempting to fit a multinomial regression using the lasso. covars is a matrix with 80 rows and roughly 4000 columns, all the covariates are binary. resp is an eight level factor. I can fit the model with no errors but when I try to cross-validate after about 30 seconds