similar to: lars - lasso problem

Hi! This is my first time posting. I've read the general rules and guidelines, but please bear with me if I make some fatal error in posting. Anyway, I have a continuous response and 29 predictors made up of continuous variables and nominal and ordinal categorical variables. I'd like to do lasso on these, but I get an error. The way I am using "lars" doesn't allow for the

hello I tried to use lars() but neither with my own data nor with the sample data it works. I get in both cases the following error prompt: > data(diabetes) > par(mfrow=c(2,2)) > attach(diabetes) > x<-lars(x,y) Error in one %*% x : requires numeric matrix/vector arguments > x<-lars(x,y, type="lasso") Error in one %*% x : requires numeric matrix/vector arguments

Dear R-users I'm trying to use lasso in lars package for subset regression, I have a large matrix of size 1000x100 and my aim is to select a subset k of the 100 variables. Is there any way in lars to fix the number k (i.e. to select the best 10 variables) library(lars) aa=lars(X,Y,type="lasso",max.steps=200) plot(aa,plottype="Cp") aa$RSS which.min(aa$RSS)

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

I'm having the exact problem outlined in a previous post from 2005 - unfortunately the post was never answered: http://tolstoy.newcastle.edu.au/R/help/05/10/15055.html When running: lm2=lars(x2,y,type="lasso",use.Gram=F) I get an error: Error in if (zmin < gamhat) { : missing value where TRUE/FALSE needed ...when running lasso via lars() on a 67x3795 set of predictors. I

When plotting a lars object, I cannot find a way to plot solid lines. Even when the arguments breaks=F and lty="solid" are used, the vertical lines at the break points do not plot but asterisks indicating the breaks still plot as part of each path leaving solid lines broken up by asterisks at the break points. I'm using the following code. larsfit <-

We have been using Least Angle Regression (lars) to help identify predictors in models where the outcome is continuous. To do so we have been relying on the lars package. Theoretically, it should be possible to use the lars procedure within a general linear model (glm) framework - we are particular interested in a logistic regression model. Does anyone have examples of using lars with logistic

Hello, I have a question about lars package, probably basic. The returned values of lars function include R squares along the variable selection path. However, such values are always slightly different from the R squares returned by the regression function lm using the same models. Anyone know the reasons? Very important, and needs quick answers. Thanks a million! -- View this message in

Hello, I have a question about the lars package. I am using this package to get the coefficients at a specific LASSO parameter s. data(diabetes) attach(diabetes) object <- lars(x,y,type="lasso") cvres<-cv.lars(x,y,K=10,fraction = seq(from = 0, to = 1, length = 100)) fits <- predict.lars(object, type="coefficients", s=0.1, mode="fraction") Can I assign

Hi, I am writing to seek some guidance regarding using Lasso regression with the R package LARS. I have introductory statistics background but I am trying to learn more. Right now I am trying to duplicate the results in a paper for shRNA prediction "An accurate and interpretable model for siRNA efficacy prediction, Jean-Philippe Vert et. al, Bioinformatics" for a Bioinformatics project

Dear all I am using lars package to do lasso in R. I dont undesrtand what max.steps do?and how I can understand from the outputs to obtain the last steps in this packagethanks for your helpbest [[alternative HTML version deleted]]

Dear All, I am using lars package written by Dr. Trevor Hastie, the version is lars_0.9-5 downloaded from cran. When I ran the diabetes example data attached in package, I found that the beta outputs from different machines are different. The difference is only about 10^-11 to 10^-12, some friends suggested that it possibly is a machine precision problem. But I check the machine numerical

Hi useR's, Has anyone used the "Lars" package in R before? If so, is there any tutorial (not manual) or worked out example online for this R package that one can go through to figure out how one can use this package with lasso regression? I appreciate any help I can get in this direction. Sincerely, Vishal -- *Vishal Thapar, Ph.D.* *Scientific informatics Analyst Cold Spring

Hi, I am trying to do a lasso regression using the lars package with the following data (see attached): FastestTime WinPercentage PlacePercentage ShowPercentage BreakAverage FinishAverage Time7Average Time3Average Finish 116.90 0.14 0.14 0.29 4.43 3.29 117.56 117.77 5.00 116.23 0.29 0.43 0.14 6.14 2.14 116.84 116.80 2.00 116.41 0.00 0.14 0.29 5.71 3.71 117.24

Hi here is the code as example lars is in package lars > x<-matrix(rnorm(20*5,0,1),20,5) > bs<-matrix(sample(seq(1:10),5),5,1) > er<-rnorm(20,0,1) > y<-x%*%bs+er > lobj<-lars(x,y,type="lasso") > names(lobj) [1] "call" "type" "df" "lambda" "R2" [6] "RSS"

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)

Hello, I have problems with 'lars' package. I found the previous post of the person who had the same issue, but the suggested solution in that post did not help me. I created the matrices: ResponseMatrix <- data.frame (GAOdecision=GAOdecision) PredictorsMatrix <- data.frame (WeaponvsNon = WeaponvsNon, ProductvsService = ProductvsService, KDuration = KDuration, BusinessSize =

Hi, I am interested in a modification to LARS that allows for positive-only constraints in the variables (with details about how to implement this as described in section 3.4 of the Efron et al (2003) LARS paper). Before I dive into the "lars" package code myself, I was wondering if anyone knew of a version where this is available, or if another package that I have not found can do

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! When I apply the lars (least-angle-regression) method to my data (3655 features, only 355 data points, no I did not mistype), I observe a strange behaviour: 1) The beta values tend to grow into real high values quite fast up to a point where they overflow and get negative. The overflow is not a problem, I don't need the last part of the analysis anyway, but why do they just