similar to: Problem with 'lars' package

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

Dear all, I met a problem when installing new packages on R, my system is linux fedora 6.0, the following is output. please help me. Thanks. > install.packages('lars') --- Please select a CRAN mirror for use in this session --- Loading Tcl/Tk interface ... done trying URL 'http://www.stathy.com/cran/src/contrib/lars_0.9-5.tar.gz' Content type 'application/x-tar' length

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"

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

Hi,   I'm using lars package to run some regression analysis and my doubt now is how can I predict my model to another dataset? Let me explain a little better: I have a dataset from which I withhold some data. With the data that wasn't withheld, I create the model. Now, what I'm not being able to do is apply the model back to the data that I withheld. Any suggestions?   Here it goes

Hi, I installed package 'lars' earlier tonight and did not get any sort of error message. ### ** building package indices ... ** testing if installed package can be loaded Loaded lars 0.9-8 * DONE (lars) ### However, when I try to load it, I get ### > library(lars, lib.loc = "~/R/x86_64-redhat-linux-gnu-library/2.11") Error : .onLoad failed in loadNamespace() for

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 Folks. This must be a silly question with a (not) obvious (to me) answer. Consider this: tmp <- matrix(1:200, nrow = 20) vec <- 300:309 tmp[9,] <- vec # replacing one row works fine p <- c(3, 11, 17) tmp[p,] <- vec # replacing multple rows pastes the values down a column and recycles vec. What I want to do is replace multiple rows simultaneously at once. I suppose I can

I'm seeing a lot of activity over the last two days with what looks to be a kiddie script. Mostly trying to access several of our servers with the username anna. All failed... in fact I don't think we have a user anna on any of our servers. Meanwhile... I'm running Sendmail. This pertains to Centos 4 and 5 servers. I'm also running fail2ban on some and Ossec on others. So far,

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

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

I am trying to extract coefficients from lars fit and can't find how to get intercept. E.g. y = rnorm(10) x = matrix(runif(50),nrow=10) X = data.frame(y,x) fit1 = lars(as.matrix(X[,2:6]),as.matrix(X[,1])) fit2 = lm(y~.,data=X) Then, if I do: > predict(fit1,s=1,mode='fraction',type='coefficients')$coef X1 X2 X3 X4 X5 0.3447570

Hi, I am trying to use lars on my data. After getting the model and did the prediction, I compare the predicted value and the coefficients and it seems like there should be a constant term in the model. I have gone through the documentation of the lars package and I can't seem to find how to get this piece of information from the lars object. Can somebody help me? Thanks a lot! Mark

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 am trying to use the lars package in R to carry out lasso analysis. However, I am having some problems. Please could you help me with the following questions: 1) Exactly what format do x and y need to be in for cv.lars(x, y) and lars (x, y)? And what information do x and y need to contain exactly? I have tried using to test just a simple matrix of numeric values for x and a simple vector of

dear all, It appears that glmnet(), when "selecting" the covariates entering the model, skips from K covariates, say, to K+2 or K+3. Thus 2 or 3 variables are "added" at the same time and it is not possible to obtain a ranking of the covariates according to their importance in the model. On the other hand lars() "adds" the covariates one at a time. My question

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

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 <-

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 =

Hello Everyone. I?m on a Mac, using rgl. Thanks to all the developers and maintainers on it! If I drag the mouse, I can rotate the view, if I hold down the option key or the ctrl key and drag, I get scaling (though at times I seem to alter the perspective?). Adding the shift key doesn't seem to modify anything. So, what I can't find is a key combination that translates the object.