similar to: R CMD check for glmpath on Windows (PR#10823)

We have uploaded to CRAN a new version of glmpath, a package which fits the L1 regularization path for generalized linear models. The revision includes: - coxpath, a function for fitting the L1-regularization path for the Cox ph model; - bootstrap functions for analyzing sparse solutions; - the ability to mix in L2 regularization along with L1 (elasticnet). We have also completed a report that

We have uploaded to CRAN a new version of glmpath, a package which fits the L1 regularization path for generalized linear models. The revision includes: - coxpath, a function for fitting the L1-regularization path for the Cox ph model; - bootstrap functions for analyzing sparse solutions; - the ability to mix in L2 regularization along with L1 (elasticnet). We have also completed a report that

Hi, I am new to model selection by coefficient shrinkage method such as lasso. And I became particularly interested in variable selection in Cox regression by lasso. I became aware of the coxpath() in R package glmpath does lasso on Cox model. I have tried the sample script on the help page of coxpath(), but I have difficult time understanding the output. Therefore, I would greatly appreciate if

Hi all, I'm relatively new to using R, and have been trying to fit an L1 regularization path using coxpath from the glmpath library. I'm interested in using a cross validation framework, where I crossvalidate on a training set to select the lambda that achieves the lowest error, then use that value of lambda on the entire training set, before applying to a test set. This seems to entail

library(glmpath) data(heart.data) # heart.data is a list, $y a vector, $x a matrix data <- data.frame(x=I(heart.data$x), y = heart.data$y) > data[1:2,] x.1 x.2 x.3 x.4 x.5 x.6 x.7 x.8 x.9 y 1 160 12 5.73 23.11 1 49 25.3 97.2 52 1 2 144 0.01 4.41 28.61 0 55 28.87 2.06 63 1 > dimnames(heart.data$x)[[2]] [1] "sbp"

I'm trying to do LASSO in R with the package glmpath. However, I'm not sure if I am using the accompanying prediction function *predict.glmpath()* correctly. Suppose I fit some regularized binomial regression model like so: library(glmpath);load(heart.data);attach(heart.data); fit <- glmpath(x, y, family=binomial) Then I can use predict.glmpath() to estimate the value of the

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the

Hi, I am analyzing a set of variables in order to create a survival model for a set of patients. I have checked the reference manual for glm path and coxpath in order to achieve it. However I have a doubt about the class of the covariates I can use with the last mentioned package. In the example, the package loads a list called "lung.data". This object has a matrix with the covariate

Dear colleagues, I hope someone can help me with my problem. I have fitted a cox model with the following syntax: # cox01def <-cph(Surv(TEVENT,EVENT) ~ ifelse(AGE>50, (AGE-50)^2,0) + BMI + # HDL+DIABETES +HISTCAR2 + log(CREAT)+ as.factor(ALBUMIN)+STENOSIS+IMT,data # = XC, x=T, y=T, surv=T) *1 Furthermore I have estimated my beta's also with a Lasso method - Coxpath ( from

Hi R-users! I am trying to learn how to use the glmpath package. I have a dataframe like this > dim(data) [1] 605 109 and selected the following > response <- data[,1] > features<-as.matrix(data[,3:109]) > mymodel <- glmpath(features,response, family = binomial) Error in if (lambda <= min.lambda) { : missing value where TRUE/FALSE expected Reading the glmpath pdf, I

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 am using glampath package for L1 regularized logistic regression. I have read the article " L1 regularization path algorithm for GLM" by park and Hastie (2006). One thing I can't understand that how to find best lambda for my prediction. I want to use that lambda for the prediction not the entire set. thanks. -- View this message in context:

Hello: I would like to obtain probability of an event for one single patient as a function of time (from survfit.coxph) object, as I want to find what is the probability of an event say at 1 month and what is the probability of an event at 80 months and compare. So I tried the following but it fails miserably. I looked at some old posts but could not figure out the solution. Here's what I did

Hello R helpers: *My first message didn't pass trough filter so here it's again* I would like to obtain probability of an event for one single patient as a function of time (from survfit.coxph) object, as I want to find what is the probability of an event say at 1 month and what is the probability of an event at 80 months and compare. So I tried the following but it fails miserably. I

----- Quick summary: In the File:Open dialog, please change "S files (*.q)" to "S files (*.q, *.ssc, *.S)" and show the corresponding files (including .SSC and .s files). ----- Background This is motivated by the following query to R-help: >Date: Thu, 16 Mar 2006 22:44:11 -0600 >From: "xpRt.wannabe" <xprt.wannabe at gmail.com> >Subject: [R] Is

I would like a way to modify the behavior of browser(), so that entering a blank line does nothing rather than being equivalent to "c". I'm running R using emacs ESS. I often debug functions by inserting a browser, and stepping through one line at a time, sending a line at a time from a buffer with the function to the R buffer. But every time I send a blank line, the browser

Hi Claire, I'm replying and CC-ing to the R-help list to get more eyes on your question since others will likely have more/better advice, and perhaps someone else in the future will have a similar question, and might find this thread handy. I've removed your specific research aim since that might be private information, but you can include that later if others find it necessary to know

timh at insightful.com wrote: > library(glmpath) > data(heart.data) > # heart.data is a list, $y a vector, $x a matrix > data <- data.frame(x=3DI(heart.data$x), y =3D heart.data$y) > =20 >> data[1:2,] >> =20 > x.1 x.2 x.3 x.4 x.5 x.6 x.7 x.8 x.9 y > 1 160 12 5.73 23.11 1 49 25.3 97.2 52 1 > 2 144 0.01 4.41 28.61

Hi List, I am trying to build an animation and facing problem as animation is not coming "live". The goal is to build "heart beats" like diagram for the data given below. data : structure(list(date = 1971:1991, x = c(41L, 60L, 41L, 61L, 22L, 83L, 31L, 55L, 22L, 14L, 38L, 6L, 29L, 17L, 10L, 99L, 88L, 32L, 68L, 78L, 26L)), .Names = c("date", "x"), class =