similar to: Major glmnet upgrade on CRAN

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

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

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

I will be giving a webinar on glmnet on Friday May 3, 2013 at 10am PDT (pacific daylight time) The one-hour webinar will consist of: - Intro to lasso and elastic net regularization, and coefficient paths - Why is glmnet so efficient and flexible - New features of the latest version of glmnet - Live glmnet demonstration - Question and Answer period To sign up for the webinar, please go to

This new version of glmnet has some bug fixes, and some new features * new arguments lower.limits=-Inf and upper.limits=Inf (defaults shown) for all the coefficients in glmnet. Users can provide limits on coefficients. See the documentation for glmnet. Typical usage: glmnet(x,y,lower=0) Here the argument is abbreviated, and by giving a single value, this uses the same value for all parameters.

This new version of glmnet has some bug fixes, and some new features * new arguments lower.limits=-Inf and upper.limits=Inf (defaults shown) for all the coefficients in glmnet. Users can provide limits on coefficients. See the documentation for glmnet. Typical usage: glmnet(x,y,lower=0) Here the argument is abbreviated, and by giving a single value, this uses the same value for all parameters.

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

This is a major revision, with two additional models included. 1) Multiresponse regression - family="mgaussian" Here we have a matrix of M responses, and we fit a series of linear models in parallel. We use a group-lasso penalty on the set of M coefficients for each variable. This means they are all in or out together 2) family="multinomial, type.multinomial="grouped"

Hi All,  I have been trying to use glmnet package to do LASSO linear regression. my x data is a matrix n_row by n_col and y is a vector of size n_row corresponding to the vector data. The number of n_col is much more larger than the number of n_row. I do the following: fits = glmnet(x, y, family="multinomial")I have been following this

Hello, I'm trying to select features of cetain numbers(like 100 out of 1000) via LASSO, based on multinomial model, however, it seems the glmnet package provides a very sparse estimation of coefficients(most of coefficients are 0), which selects very few number of variables, like only 10, based on my easy dataset. I try to connect the choice of lambda to the selecting

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

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit 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

SoftImpute is a new package for matrix completion - i.e. for imputing missing values in matrices. SoftImpute was written by myself and Rahul Mazumder. softImpute uses uses squared-error loss with nuclear norm regularization - one can think of it as the "lasso" for matrix approximation - to find a low-rank approximation to the observed entries in the matrix. This low-rank approximation

SoftImpute is a new package for matrix completion - i.e. for imputing missing values in matrices. SoftImpute was written by myself and Rahul Mazumder. softImpute uses uses squared-error loss with nuclear norm regularization - one can think of it as the "lasso" for matrix approximation - to find a low-rank approximation to the observed entries in the matrix. This low-rank approximation

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

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