similar to: glmnetUtils 1.1 now available

Dear R-experts, Here below my R code with an error message. Can somebody help me to fix this error?? Really appreciate your help. Best, ############################################################ #?MSE CROSSVALIDATION Lasso regression? library(glmnet) ?

No error message shown Please include the error message so that it is not necessary to rerun your code. This might enable someone to see the problem without running the code (e.g. downloading packages, etc.) -- Bert On Sun, Oct 22, 2023 at 1:36?PM varin sacha via R-help <r-help at r-project.org> wrote: > > Dear R-experts, > > Here below my R code with an error message. Can

Dear R-experts, I really thank you all a lot for your responses. So, here is the error (and warning) messages at the end of my R code. Many thanks for your help. Error in UseMethod("predict") : ? no applicable method for 'predict' applied to an object of class "c('matrix', 'array', 'double', 'numeric')" > mean(unlist(lst)) [1] NA

?s 20:12 de 23/10/2023, varin sacha via R-help escreveu: > Dear R-experts, > > I really thank you all a lot for your responses. So, here is the error (and warning) messages at the end of my R code. > > Many thanks for your help. > > > Error in UseMethod("predict") : > ? no applicable method for 'predict' applied to an object of class

Hi Everybody! I must start with a declaration that I am a sparse user of R. I am creating a credit scorecard using a dataset which has a variable depicting actual credit history (good/bad) and 41 other variables of yes/no type. The procedure I am asked to follow is to use a penalized logistic procedure for variable selection. I have located the package "glmnet" which gives the complete

For what it's worth it looks like spm2 is specifically for *spatial* predictive modeling; presumably its version of CV is doing something spatially aware. I agree that glmnet is old and reliable. One might want to use a tidymodels wrapper to create pipelines where you can more easily switch among predictive algorithms (see the `parsnip` package), but otherwise sticking to glmnet

Hello, I am attempting to use elasticnet to classify a number of documents. The features are words. The data is coded into a matrix with each document as a row and each word as a column. The data is binary, with {0,1} indicating the presence of a word. I want to use the cross validation function of elasticnet (cv.enet). However, when the code selects a random subset of the data for a given

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

Hello, Thank you for probably not so new question, but i am new to R. Does any of packages have something like glm+regularization? So far i see probably something close to that as a ridge regression in MASS but I think i need something like GLM, in particular binomial regularized versions of polynomial regression. Also I am not sure how some of the K-fold crossvalidation helpers out there

Hi, perhaps you can help me to find out, how to find the best Lambda in a LASSO-model. I have a feature selection problem with 150 proteins potentially predicting Cancer or Noncancer. With a lasso model fit.glm <- glmpath(x=as.matrix(X), y=target, family="binomial") (target is 0, 1 <- Cancer non cancer, X the proteins, numerical in expression), I get following path (PICTURE

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

Hi,I'm wondering if anyone has run into this but I found out that I can't use Window's VPN connection and logon to Samba using Aliases. I can log into the server using the server's main NetBIOS name but I can't use any of the aliases. Whilst inside the network the aliases work fine.Any ideas on how to get around this?# Samba config file created using SWAT # from 192.168.1.200

Hi, I have come to my wits end again (lately, it's a very short trip). I have been trying to connect to Samba 3.0.21b but Windows keeps throwing up the logon dialog. I'm using "Map network drive" to try to mount this share. It seems the problem is only on the Windows side since I have tested the connection through the Unix account. Both user id and password work fine.

Hi, Problem: Out of the blue Vista on my laptop decides it doesn't want to mount a share over PPTP VPN connection. I can mount the share in the local network but I can't do it over VPN. When trying to access the server, using "net view \\172.16.1.71" it shows the error in the subject. Platform/Versions: OS: Sun Solaris 10 11/06 release Samba version:

Hi, I am trying to do logistic regression for data of 104 patients, which have one outcome (yes or no) and 15 variables (9 categorical factors [yes or no] and 6 continuous variables). Number of yes outcome is 25. Twenty-five events and 15 variables mean events per variable is much less than 10. Therefore, I tried to analyze the data with penalized regression method. I would like please some of the

Hi all, I am using the glmnet R package to run LASSO with binary logistic regression. I have over 290 samples with outcome data (0 for alive, 1 for dead) and over 230 predictor variables. I currently using LASSO to reduce the number of predictor variables. I am using the cv.glmnet function to do 10-fold cross validation on a sequence of lambda values which I let glmnet determine. I then take

Can anybody help me to run the Elasticnet package of R to build some model, i am a freshman to R language , when i use the Elasticnet package to my data, it always reture a error, but i can't settle that problem. I consider if there is any constrant of the data to that package? Can anyone help me to run the elasticnet and check my data as you convenient? I put the data in attachment. Thank

Hi everyone, Can someone please help me in these questions?: 1)if I use crossvalidation with svm, do I have to use this equation to calculate RMSE?: mymodel <- svm(myformula,data=mydata,cross=10) sqrt(mean(mymodel$MSE)) But if I don’t use crossvalidation, I have to use the following to calculate RMSE: mymodel <- svm(myformula,data=mydata) mytest

I use rsync on an SSD which is slow at writing, but very fast at reading, so it is important to me to avoid writing data. I had hoped rsync would only write changes in files when using --inplace, but it seems not: # Make a 1 GB file $ perl -e 'print "x"x1000000000' > 1g # rsync it (as expected this costs 1 GB writes) $ ./rsync-3.1.0/rsync --progress --inplace -cva 1g 1g.copy