similar to: estimating survival times with glmnet and coxph

Dear all, I am confused with the behaviour of survfit with newdata option. I am using the latest version R-2-15-0. In the simple example below I am building a coxph model on 90 patients and trying to predict 10 patients. Unfortunately the survival curve at the end is for 90 patients. Could somebody please from the survival package confirm that this behaviour is as expected or not - because I

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

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

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 I'm having trouble using lars and glmnet functions to predict on a new data set with different nrow then the original : for instance: ============= log.1 = glm(temp.data$TL~(.),temp.data,family = binomial,x=TRUE,y=TRUE) nrow(test.data) != nrow(temp.data # == TRUE Val.frame = model.frame(log.1,test.data) # returns a data frame with the variables needed to use log.1

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

Hi, I was trying to install package glmnet in R, but failed and it show such messages: * Installing *source* package glmnet ... This package has only been tested with gfortran. So some checks are needed. R_HOME is /home/username/R/R-2.9.0 Attempting to determine R_ARCH... R_ARCH is Attempting to detect how R was configured for Fortran 90.... Unsupported Fortran 90 compiler or Fortran 90

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 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 Robert, The differences have to do with diffent scaling defaults. lrm by default standardizes the covariates to unit sd before applying penalization. penalized by default does not do any standardization, but if asked standardizes on unit second central moment. In your example: x = c(-2, -2, -2, -2, -1, -1, -1, 2, 2, 2, 3, 3, 3, 3) z = c(0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1) You

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, 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:

Dear List, I'm running simulations using the glmnet package. I need to use an 'automated' method for model selection at each iteration of the simulation. The cv.glmnet function in the same package is handy for that purpose. However, in my simulation I have p >> N, and in some cases the selected model from cv.glmet is essentially shrinking all coefficients to zero. In this case,

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, R version 2.8.0 (2008-10-20) on a linux-64-bit machine. I am trying to install the "glmnet" package and I get the following error: ******************************************************************************* > install.packages(pkgs = "glmnet") trying URL 'http://cran.parentinginformed.com/src/contrib/glmnet_1.1-5.tar.gz' Content type