similar to: lasso for AFT model

Hi, In the package survival, using the function survreg for AFT model, I only see 4 distributions for the response y: weibull, gaussian, logistic, lognormal and log-logistic, which correspond to certain distributions for the error terms. I'm wondering if there is a package or how to obtain the parameter estimates (the beta's are of great interest) from the AFT model (maximizing

Hi, I'm estimating a loglogistic aft (accelerated failure time) model, just a simple plain vanilla one (without time dependent covariates), I'm comparing the results that I obtain between aftreg (eha package) and survreg(surv package). If I don't use any covariate the results are identical , if I add covariates all the coefficients are the same until a precision of 10^4 or 10^-5 except

Dear all: I have a question about how to get the optimal estimate of coefficients using the penalized quantile regression (LASSO penalty in quantile regression defined in Koenker 2005). In R, I found both rq(y ~ x, method="lasso",lambda = 30) and rq.fit.lasso(x, y, tau = 0.5, lambda = 1, beta = .9995, eps = 1e-06) can give the estimates. But, I didn't find a way using either of

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

Hi, all, I am using the lars package for lasso estimate. So I get a lasso fit first: lassofit = lars(x,y,type ="lasso",normalize=T, intercept=T) Then I want to get coefficient with respect to a certain value of \lambda (the tuning parameter), I know lars has three mode options c("step", "fraction", "norm"), but can I use the \lambda value instead

Dear R-community, Dear Prof. Therneau, I would like to fit an AFT-model with time-dependent covariates and right-censored data. Searching the mailing list for information on the subject, I found some old posts which said it didn't work back then. My questions: (1) Has this kind of fitting already been implemented in the survival library in R? (2) If not: Are there any alternatives/

I wish to estimate sparse causal networks from simulated time series data. Although there's some discussion about this problem in the literature (at least a few authors have used lasso and l(1,2) regularization to enforce sparsity in multivariate autoregressive models, e.g., http://user.cs.tu-berlin.de/~nkraemer/papers/grplasso_causality.pdf), I can't find any R packages with these

I have fit an accelerated failure time model using coxph, and have what seems to be a simple question that I can't figure out. Given a vector of predictor values X, the survival time S[t|X] is the probability the entity will survive longer than some time t. Now, how do I calculate this for a specific value of t? -- View this message in context:

Dear Prof. Brostr?m, Dear R-mailinglist, first of all thanks a lot for your great effort to incorporate time-varying covariates into aftreg. It works like a charm so far and I'll update you with detailled benchmarks as soon as I have them. I have one more questions regarding Accelerated Failure Time models (with aftreg): You mention that left truncation in combination with time-varying

Hi everyone, We have been trying to construct a Lasso-cox model with discrete time. We conducted follow-up examinations on the epileptic attack after tumor surgical resection among glioma patients. The patients are followed-up in the 6/12/24 months after surgical resection, which makes the epilepsy-free time discrete (6/12/24 months). We calcluated many features from the T2-images

Dear list, I have been trying to apply a simple lasso regression on a 10-element vector, just to see how this method works so as to later implement it on larger datasets. I thus create an input vector x: * x=rnorm(10)* I add some noise * noise=runif(n=10, min=-0.1, max=0.1)* and I create a simple linear model which calculates my output vector y * y=2*x+1+noise* I then do

Totally understood. If you remain "on vacation" (we wish eh?!) and get further messages like that from the list s'ware, just accept my standing apologies and delete them. Very welcome re letter! Very best, Chris ------- Forwarded message follows ------- Send reply to: "Alana O'C" <alanaoc at internode.on.net> From: "Alana O'C"

Hi all. Sorry for the long email. I have been trying to find someone local to work on this with me, without much luck. I went in to our local stats consulting service here, and the guy there told me that I already know more about model selection than he does. :-< He pointed me towards another professor that can perhaps help, but that prof is busy until mid-June, so I want to get as much

I also hope to get the code of using lasso method in the cox model.Could you please send me one? Thank you so much!!!

In the package lasso2, there is a Prostate Data. To find coefficients in the prostate cancer example we could impose L1 constraint on the parameters. code is: data(Prostate) p.mean <- apply(Prostate, 5,mean) pros <- sweep(Prostate, 5, p.mean, "-") p.std <- apply(pros, 5, var) pros <- sweep(pros, 5, sqrt(p.std),"/") pros[, "lpsa"] <-

Hi, I know how to use LASSO for model selection based on the Cp criterion. I heard that we can also use cross validation as a criterion too. I used cv.lars to give me the lowest predicted error & fraction. But I'm short of a step to arrive at the number of variables to be included in the final model. How do we do that? Is it the predict.lars function? i tried >

Hi all, I have a data set containing variables LOSS, GDP, HPI and UE. (I have attached it in case it is required). Having renamed the variables as l,g,h and u, I wish to run a Lasso Regression with l as the dependent variable and all the other 3 as the independent variables. data=read.table("data.txt", header=T) l=data$LOSS h=data$HPI u=data$UE g=data$GDP matrix=data.frame(l,g,h,u)

Dear R community, I'm curious to know how people go about estimating standard errors for parameter estimates after model selection by ridge regression and the lasso. Do you have any practical or theoretical advice? Warmly, Andrew -- Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-9763 University of Melbourne, VIC 3010 Australia Fax:

I'm using the package 'lars' in R with the following code: > library(lars) > set.seed(3) > n <- 1000 > x1 <- rnorm(n) > x2 <- x1+rnorm(n)*0.5 > x3 <- rnorm(n) > x4 <- rnorm(n) > x5 <- rexp(n) > y <- 5*x1 + 4*x2 + 2*x3 + 7*x4 + rnorm(n) > x <- cbind(x1,x2,x3,x4,x5) > cor(cbind(y,x)) y x1 x2