similar to: Check functions in package

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",

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

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=',');

Hi all, I have been experimenting with writing my own matrix column sum function. I want it to return a list. csum<-function(m) { a = data.frame(m) s = lapply(a,sum) return(s) } I wish to use the same code up until the return(s) that I have listed above. The problem is that s, I believe, is a data frame (looks like this:) $X1 [1] 148 $X2 [1] 156 $X3 [1] 164 $X4 [1] 172

Alright, so I am trying to write my own function to calculate column sums in a matrix. I want the result as a single list with the values. So far I have: csum<-function(m) { a = data.frame(m) s = lapply(a,sum) return(s) } What is the easiest way to have it return in a format such as [1] 6 15 24 ? Thanks. -- View this message in context:

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

I have a set of data (in a matrix). I spliced a column out and parsed it as.ts (time series). I then plotted the time series but I found that it was very noisy. I wanted to smooth it out. However, I am having some problems smoothing and plotting the smoothed version. > A <- as.ts(read.table(choose.files())) > x <- as.ts(A[,10]) > plot(x) > > plot(smooth(x))

Hi, I created a logistic regression model using the glmnet package. This model is of class "glmnet" or "lognet". I wanted to plot a calibration curve for this model using the calibrate() function from rms package, but the objects used are different, rms requires a fit from lrm(). Is there another function for getting the calibration plot for this glmnet object, or can anyone

Hi all I am using the caret package and having difficulty in obtaining the results using regression, I used the glmnet to model and trying to get the coefficients and the model parameters I am trying to use the extractPrediction to obtain a confusion matrix and it seems to be giving me errors. x<-read.csv("x.csv", header=TRUE); y<-read.csv("y.csv", header=TRUE);

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

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

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

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 have a problem with creating an identity matrix for glmnet by using the contrasts function. I have a factor with 4 levels. When I create dummy variables I think there should be n-1 variables (in this case 3) - so that the contrasts would be against the baseline level. This is also what is written in the help file for 'contrasts'. The problem is that the function

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

All, For my understanding, I wanted to see if I can get glmnet predictions using both the predict function and also by multiplying coefficients by the variable matrix. This is not worked out. Could anyone suggest where I am going wrong? I understand that I may not have the mean/intercept correct, but the scaling is also off, which suggests a bigger mistake. Thanks for your help. Juliet Hannah

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 all, I am having trouble with the glmnet package. I used it for the first time last week, after updating R to the current version (2.10.1, running in Windows XP). It seemed to be working fine as I explored what it could do with one of my datasets. A day or two later, using the exact same dataset, it suddenly stopped working. I get the following error every time I try to use glmnet with

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