similar to: Big Data reading subsample csv

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

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 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

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

R-help readers, I''m having a problem with an R script (see below), which regularly generates the error message, Error in start:(start + (sample.length - 1)) : NA/NaN argument , for which I am unsure of the cause. In essence, the script (below) generates the start and end points for random subsamples from along a vector (in reality a transect (of a given length,

Hello I apologise for the length of this entry but please bear with me. In short: I need a way of subsampling communities from all possible communities of n taxa taken 1:n at a time without having to calculate all possible combinations (because this gives me a memory error - using combn() or expand.grid() at least). Does anyone know of a function? Or can you help me edit the combn or

Hello R-users, I am trying to obtain Type III SS for an ANOVA with subsampling. My design is slightly unbalanced with either 3 or 4 subsamples per replicate. The basic aov model would be: fit <- aov(y~x+Error(subsample)) But this gives Type I SS and not Type III. But, using the drop() option: drop1(fit, test="F") I get an error message: "Error in

hi, I would like to subsample the array c(1:200) at random into ten subsamples v1,v2,...,v10. I tried with to go progressively like this: > x<-c(1:200) > v1<-sample(x,20) > y<-x[-v1] > v2<-sample(y,20) and then I want to do: >x<-y[-v2] Error: subscript out of bounds.

Hi All,  I am trying to run cv.glmnet(x,y,family="multinomial", nfolds =4) and I only have 8 observations and the number of features I have is 1000, so my x matrix is 8 by 1000 and when I run the following, I get this error, I am not sure what is causing this problem.  Error in predmat[which, , seq(nlami)] = preds :   number of items to replace is not a multiple of replacement length Can

Dear useR's, today I stumbled over an interesting phenomenon: First, I created a named numeric vector with a certain class and several attributes via the structure() function. After that, I implemented a simple print method for this class. When calling this function it produces an endless loop of print calls until R crashes. :/ What is going on here? Is this a bug or have I done something

Dear all, I would like to randomly split a sample in two equally large subsamples. The sample data is stored as a matrix with each row representing an individual and each column representing some variable (e.g., name, age, sex, etc.); the first row contains the names of the variables; the first column contains the individual number (1:n, for n individuals); the number of individuals is even (so,

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

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

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