similar to: lasso plot using LARS

I want to be able to load a library in a specified position using the pos= argument and have any subsequent library required by the one I'm loading go into a specified library as well. For example, in loading caret, it requires and loads lattice as well. When I specify that caret goes into position 9, lattice goes into position 2 by default. Is there a way to specify that by loading a

Dear R users, Is documentation for the netlabR package available in English? If not does anyone know if or when it will be? Regards, Keith ****************************************************************************This e-mail and any files transmitted with it are confidential and are intended solely for the use of the individual or entity to whom it is addressed. If you are not

I have a data set called datastep4 with 211484 rows and 95 columns > dim(datastep4) [1] 211484 95 The first few column names are given below, note the first one is "RESPONDED" > names(datastep4)[1:5] [1] "RESPONDED" "VAR_30" "VAR_31" "VAR_32" "VAR_33" A table of RESPONDED shows mostly zeros >

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 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]]

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

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

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

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

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

I have a longitudinal data set in long format and I want to run individual regressions. I do this by using the by() function as follows: temp <- by(tolerance.pp, tolerance.pp$id, function(x) summary(lm(tolerance ~ time, data=x))) This works fine. Coefficients for the first two subjects are shown below. Coefficients: Estimate Std. Error t value Pr(>|t|)

On Thu, 26 Feb 2015 22:45:02 +0000 Rowland Penny <rowlandpenny at googlemail.com> wrote: > > Just what do you mean by 'allow no connections' ?? where from ? how? ivory:~ wynkoop$ smbclient -L prd2 Receiving SMB: Server stopped responding protocol negotiation failed ivory:~ wynkoop$ and on the server with the samba-tool built config I have these samba related processes:

On Thu, 18 Dec 2014 09:49:28 -0800 Jeremy Allison <jra at samba.org> wrote: winbind trusted domains only = yes > > > > server services = rpc, nbt, wrepl, ldap, cldap, kdc, drepl, > > winbind, nt p_signd, kcc, smb > > Remove 'smb' from this line. In fact, remove this line > completely. Where did you find docs telling you to add > this

On Wed, 25 Feb 2015 19:48:07 +0000 Rowland Penny <rowlandpenny at googlemail.com> wrote: > OK, you would appear to be running samba4 in AD mode, i.e. you > provisioned it. > > You have now tried to add things to your smb.conf to make it work > like samba3, this will not work! Put your smb.conf back to what it > was like just after the provision and then go and read the

The rvm function from the kernlab package can only be used for regression at the present time. In fact, in the description in the kernlab documentation for the type argument for rvm says, "type rvm can only be used for regression at the moment". Are there any R packages that do classification with relevance vector machines? Keith

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)

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

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

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