similar to: stats glm Response Format Ambiguity

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

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

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:

glmnet_1.2 has been uploaded to CRAN. This is a major upgrade, with the following additional features: * poisson family, with dense or sparse x * Cox proportional hazards family, for dense x * wide range of cross-validation features. All models have several criteria for cross-validation. These include deviance, mean absolute error, misclassification error and "auc" for logistic or

glmnet_1.2 has been uploaded to CRAN. This is a major upgrade, with the following additional features: * poisson family, with dense or sparse x * Cox proportional hazards family, for dense x * wide range of cross-validation features. All models have several criteria for cross-validation. These include deviance, mean absolute error, misclassification error and "auc" for logistic or

Dear all, If I am not mistaken, I think that I have found a bug in glmnet 1.7.1 (latest version) for multinomial when alpha=0. Here is the code > library(glmnet) Loading required package: Matrix Loading required package: lattice Loaded glmnet 1.7.1 > x=matrix(rnorm(40*500),40,500) > g4=sample(1:7,40,replace=TRUE) > fit=glmnet(x,g4,family="multinomial",alpha=0) >

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

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, I'm most grateful for your time to read this. I have a uber size 30GB file of 6 million records and 3000 (mostly categorical data) columns in csv format. I want to bootstrap subsamples for multinomial regression, but it's proving difficult even with my 64GB RAM in my machine and twice that swap file , the process becomes super slow and halts. I'm thinking about generating

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

On 25.07.2018 04:00, Dario Strbenac wrote: > Good day, > > A self-contained example which reproduces the problem is > > packagesFolder <- tempdir() > latestFolder <- file.path(packagesFolder, "latest") > dir.create(latestFolder) > > devtools::create(file.path(latestFolder, "statistics"), description = list(Version = "1.1.0", Depends

Good day, I believe from the documentation that folders in R_LIBS should appear in the output of .libPaths, but they do not. The documentation contains "The library search path is initialized at startup from the environment variable R_LIBS ..." However, $ export R_LIBS=/users/stgrad/dario/tmp/ $ Rscript -e ".libPaths()" [1] "/dskh/nobackup/biostat/Bioconductor"

Good day, If the library path is changed with .libPaths, the command install.packages("/nb/dario/Biostrings_2.39.9.tar.gz", repos=NULL) fails with ** testing if installed package can be loaded Error : package ?S4Vectors? required by ?Biostrings? could not be found However, running library(S4Vectors) followed by sessionInfo() after the error shows that the package can indeed be

Good day, The use of "rounding" also doesn't make sense. If The number is halfway between two integers, it is rounded to the nearest even integer. > round(2.5) [1] 2 -------------------------------------- Dario Strbenac University of Sydney Camperdown NSW 2050 Australia

Sorry, I was dragging the laptop's cursor towards the Discard button but it activated on Send instead. Please ignore or delete from mailing archive. ________________________________________ From: Martin Maechler <maechler at stat.math.ethz.ch> Sent: Monday, 23 March 2020 11:10 PM To: Dario Strbenac Subject: Re: [Rd] Grid pmax Unusual Result In Alpha Version >>>>> Dario

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

Good day, I'm trying the example cl <- makeCluster(2, type = "SOCK") from the makeCluster documentation and the R command prompt never returns. I am using a 32-bit Windows 10 computer. The problem doesn't happen on another 64-bit Windows 10 computer but does happen on another 32-bit Windows 7 computer. Can anyone reproduce it? Are there any other options that can be passed to

>>>>> Michael Lawrence <lawrence.michael at gene.com> >>>>> on Tue, 30 Jan 2018 10:37:38 -0800 writes: > I agree that it would make sense for the object to have c("by", "list") as > its class attribute, since the object is known to behave as a list. Well, but that (list behavior) applies to most non-simple S3 classed