similar to: R segfaulting with glmnet on some data, not other

Hi, I am trying to do multinomial regression using the glmnet package, but the following gives me an error (for no reason apparent to me): library(glmnet) cv.glmnet(x=matrix(c(1,2,3,4,5,6,1,2,3,4,5,6), nrow=6),y=as.factor(c(1,2,1,2,3,3)),family='multinomial',alpha=0.5, nfolds=2) The error i get is: Error in if (outlist$msg != "Unknown error") return(outlist) : argument is of

Hi, My apologies for asking this question, but could not find the answer elsewhere. I understand the glmnet package uses Fortran code. For example, the lognet.R file includes the lines of code shown below. But how can I see the Fortran code that is being referenced in the code below? Is that provided somewhere in the package source code? .Fortran("lognet",

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

Could any one help ? I start to learn the glmnet package. I tried with the example in the manual. x=matrix(rnorm(100*20),100,20) y=rnorm(100) fit1=glmnet(x,y) When I tried to fit the model, I received the error message: Error in validObject(.Object) : invalid class "dgCMatrix" object: row indices are not sorted within columns Thank you very much!

Dear Robert, The differences have to do with diffent scaling defaults. lrm by default standardizes the covariates to unit sd before applying penalization. penalized by default does not do any standardization, but if asked standardizes on unit second central moment. In your example: x = c(-2, -2, -2, -2, -1, -1, -1, 2, 2, 2, 3, 3, 3, 3) z = c(0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1) You

The following R code using different packages gives the same results for a simple logistic regression without regularization, but different results with regularization. This may just be a matter of different scaling of the regularization parameters, but if anyone familiar with these packages has insight into why the results differ, I'd appreciate hearing about it. I'm new to

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

Dear All, I want to generate survival curve with cox model but I want to estimate the coefficients using glmnet. However, I also want to include a strata() term in the model. Could anyone please tell me how to have this strata() effect in the model in glmnet? I tried converting a formula with strata() to a design matrix and feeding to glmnet, but glmnet just treats the strata() term with one

Hello, I?m trying to in install the package 'glmnet' but I get always the error massage "package ?Matrix? is not available". I search on you site, but I coundn?t find the package there either. Is their still a package called "Matrix"? Or how can I use "glmnet"? Thank You in advance. Kind regards J.Hemmersbach

Hi there, I am trying to use predict() with an object returned by cv.glmnet(), and get the following error: no applicable method for 'predict' applied to an object of class "cv.glmnet" What's wrong? my code: x=matrix(rnorm(100*20),100,20) y=rnorm(100) cv.fit=cv.glmnet(x,y) predict(cv.fit,newx=x[1:5,]) coef(cv.fit) Thanks so much, Asaf -- View this message in context:

dear all, It appears that glmnet(), when "selecting" the covariates entering the model, skips from K covariates, say, to K+2 or K+3. Thus 2 or 3 variables are "added" at the same time and it is not possible to obtain a ranking of the covariates according to their importance in the model. On the other hand lars() "adds" the covariates one at a time. My question

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

Hello, I got an error message saying Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : NA/NaN/Inf in foreign function call (arg 5) when I try to analysis a binary trait using glmnet(R) by running the following code library(glmnet) Xori <- read.table("c:\\SNP.txt", sep='\t'); Yori <- read.table("c:\\Trait.txt", sep=',');

Dear All, Can the x matrix in the glmnet() function of glmnet package be a data.frame with numeric columns and factor columns? I am asking this because I have a model with both numeric and categorical predictors, which I would like to study with glmnet. I have already tried to use a data.frame, but with no success -- as far as I know, the matrix object can only have data of a single type. Is

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

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

Dear R users, if all my numerical variables in my datasets having the same units, may I leave them unnormalized, just do cv.glmnet directly(cv.glmnet(data,standardize=FALSE))? i know normally if there is a mixture of numerical and categorical , one has to standardize the numerical part before applying cv.glmnet with standardize=fase, but that's due to the different units in the numerical

Hi, all: Does anybody know how to avoid the intercept term in cv.glmnet coefficient? When I say "avoid", it does not mean using coef()[-1] to omit the printout of intercept, it means no intercept at all when doing the analysis. Thanks. [[alternative HTML version deleted]]

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