similar to: coxpath() in package glmpath

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

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'm trying to using pspline in bic.surv{BMA}. ############################# library(BMA) library(survival) data(veteran) test.bic.surv<- bic.surv(Surv(time,status) ~ karno+pspline(age,df=3)+diagtime+prior, data = veteran, factor.type = TRUE) summary(test.bic.surv, conditional=FALSE, digits=2) ############################# The results are:

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

The problem first appeared in R 2.6.1 and is still there in R 2.6.2 On Windows running R CMD check command for glmpath package fails. The reason seems to be that when R is running the examples file (glmpath-Ex.R), it skips about 50 lines and as a result gives a syntax error. I'm working with a modified version of the CRAN glmpath 0.94. My version happens to give a more clear example of a

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

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

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

R Folk: Please forgive what I'm sure is a fairly na?ve question; I hope it's clear. A colleague and I have been doing a really simple one-off survival analysis, but this is an area with which we are not very familiar, we just happen to have gathered some data that needs this type of analysis. We've done quite a bit of reading, but answers escape us, even though the question below

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

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 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

Dear Prof. Therneau I was impressed to discover that the 'survConcordance' now handles Surv() objects in counting format (example below to clarify what I mean). This is not documented in the help page for the function. I am very curious to see how a c-index is estimated in this case, using just the linear predictors. It was my impression that with left truncation the ordering of

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:

--- included message ---- Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the rpart package)* ---end ---- Consider the following printout from rpart summary(rpart(time ~ age + ph.ecog + pat.karno, data=lung)) Node number 1: 228 observations,

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

Hi, I am trying to use the rpart library with my own set of functions on a survival object. I get an immeadiate segmentation fault when i try calling rpart with my list of functions. I get the same problem with the logrank example from Therneau,s S-rpart library though their anova example works. Should I report this as a bug, as even if my functions are structured improperly, that should lead to