similar to: Problem with lars

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

Hi! I have the following issues using "R for Mac OS X 2.2.1" (from the ppc-.dmg) on a Mac OS X 10.4.5: 1) The command history is never saved (automatically) to .Rhistory. (The file stays empty.) 2) If I save a history to some file via the "Save History" button, and I load it afterwards via "Load History", only the first entry of the saved History appears in

Dear listers, I'm using gam(from mgcv) for semi-parametric regression on small and noisy datasets(10 to 200 observations), and facing a problem of overfitting. According to the book(Simon N. Wood / Generalized Additive Models: An Introduction with R), it is suggested to avoid overfitting by inflating the effective degrees of freedom in GCV evaluation with increased "gamma"

Hi! I want o calc the norm of a complex number ( |c| where c is complex). Does the function abs() this ? Thanks, -- Frank G. Zoellner AG Angewandte Informatik Technische Fakult"at Universit"at Bielefeld phone: +49(0)521-106-2951 fax: +49(0)521-106-2992 email: fzoellne at techfak.uni-bielefeld.de

Hi, I changed something in the package "climatol" especially in the function "diagwl". Dos anybody know where I can put the code in the Internet to discuss it? I made from "C" for gradcelsius "?C" with: unit <- list( temp = expression (paste(#"T ", degree,"C")) ) mtext(unit$temp ,2,col=tcol,las=1,line=3,adj=0,at=55)

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

Hi! I am using the fft() function the base package to transform some 1d signal. If I use this standar fucntion I get a very huge first fourier coeficient. I think this dues to the handling of the borders of the signal. Usually in fft especially in image processing the signal is simulated to be continuous by adding the signal several times periodically. My question is, is there some function

I uploaded a new version of the lars package to CRAN, which incorporates some nontrivial changes. 1) lars now has normalize and intercept options, both defaulted to TRUE, which means the variables are scaled to have unit euclidean norm, and an intercept is included in the model. Either or both can be set to FALSE. 2) lars has an additional type = "stepwise" option; now the list is

I uploaded a new version of the lars package to CRAN, which incorporates some nontrivial changes. 1) lars now has normalize and intercept options, both defaulted to TRUE, which means the variables are scaled to have unit euclidean norm, and an intercept is included in the model. Either or both can be set to FALSE. 2) lars has an additional type = "stepwise" option; now the list is

The subject is a Generalized Additive Model. Experts caution us against overfitting the data, which can cause inaccurate results. I am not a statistician (my background is in Computer Science). Perhaps some kind soul would take a look and vet the model for overfitting the data. The study estimated the ebb and flow of traffic through a voting place. Just one voting place was studied; the

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

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

I got my posting bounced and sorry if I accidentally post twice. I have been looking at 'lars' pkg and got puzzled by the behavior of function 'lars'. I want to do weighted lasso regression and can't get a match from lars output with lm output. Here is an example: y = rnorm(10) x = matrix(runif(50),nrow=10) X = data.frame(y,x) z = runif(10) X = data.frame(y,x,z) X$z = X$z /

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

Hi, I'm having trouble running 'lars'. When I install it I get the following warning: >install.packages('lars') Warning in install.packages("lars") : argument 'lib' is missing: using 'C:\Users\Anna\Documents/R/win-library/2.10' --- Please select a CRAN mirror for use in this session --- trying URL

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"

Dear all, I am using modified LARS algorithm (ref: The Adaptive Lasso and Its Oracle Properties, Zou 2006) for adaptive lasso penalized linear regression. 1. w(j) <- |beta_ols(j)|^(-gamma) gamma>0 and j = 1,...,p 2. define x_new(j) <- x(j)*w(j) 3. apply LARS to solve modified lasso problem out.adalasso <- lars(X_new,y,type="lasso") or enet(X_new,

Hi! I am using R 1.8.1 and the svm of the e1071 package for classification. The problem is that I have unbalanced classes e.g. the first one is much bigger than the second one and therfore the svm is biased to the first class. If I manually adjust the class size the bias disappears. The question is then how to include this unequal class distribution to the svm (e.g. via wheights or costs)?

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