similar to: step, leaps, lasso, LSE or what?

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 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

Greetings list, It's been a while since I've been able to focus on asterisk packaging but this weekend I took some time to audit and recompile packages for CentOS 4.2. You can find them here. ftp://ftp.linuxsys.com/ftp/pub/releases/CentOS-4.0 You have your choice of 1.2.1 or 1.0.10 releases. If you need zaptel modules then install this kernel as well:

R-packages leaps and subselect implement various methods of selecting best or good subsets of predictor variables for linear regression models, but they do not seem to be applicable to logistic regression models. Does anyone know of software for finding good subsets of predictor variables for linear regression models? Thanks. -Ben p.s., The leaps package references "Subset Selection

Hi there, I would like to ask something about how to avoid a possible error message within a for loop. I am running a simulation and in some repetitions there may be an error that will cause a crash and stop the whole procedure, what I want is to simply move on to the next iteration automatically and discard the "bad" repetitions from my results. I used the "try" function to

Available in the usual place. ftp://ftp.linuxsys.com/pub/releases/CentOS-4.0 This release includes minor spec changes, spandsp 0.0.2pre23, a new Sangoma wanpipe RPM for use with the LSE kernel rpm and an AMP installation document. Best Regards, -- Andrew McRory - President/CTO Linux Systems Engineers, Inc. - http://www.linuxsys.com Located in beautiful Tallahassee, Florida Office

Dear All, Probably it is not highly relevant question: Why do stepwise regression functions in R (step() or stepAIC()) add/delete categorical variables as a set? For example, I have a four-level factor variable d, so dummies are d1,d2,d3, as stepwise regression operates d, adding or removing, d1,d2,d3 are simultaneously added/removed. What's the concern here if operating dummies individually?

Hi All, I am a newbie and trying to install Asterisk from instructions given in http://www.voip-info.org/tiki-index.php?page=Asterisk+RPM. We have Centos 3.3 so I downloaded rpm's from ftp://ftp.linuxsys.com/pub/LSE/packages/CentOS-3.4/asterisk-1.0.9/ and tried installing one by one but I get the following errors error: Failed dependencies: asterisk = v1.0.9 is needed by

Dear all i?m working with a program i?ve made in R (using functions that others created) to run my program i need a sample. if i generate the sample using for example, rnorm(n, mu, sigma) i have no problem but if i obtain a sample from a column in excel and i copy it, the program says that there is a mistake: it says "Error en `[.data.frame`(data, indices) : undefined columns

Hi, I was recently plowing through the docs of the quantreg package by Roger Koenker and came across the total variation penalty approach to 1-dimensional spline fitting. I googled around a bit and have found some papers originated in the image processing community, but (apart from Roger's papers) no paper that would discuss its statistical aspects. I have a couple of questions in this

Hi all, In Tibshirani's PNAS paper about nearest shrunken centroid analysis of microarrays (PNAS vol 99:6567), they used cross validation to choose the amount of shrinkage used in the model, and then test the performance of the model with the cross-validated shrinkage in separate independent testing set. If I don't have the luxury of having independent testing set, can I just use the

Hi, I have optimized the shrinkage parameter (GCV)for ridge and got my r2 value is 70% . to check the sensitivity of the result, I did permutation test. I permuted the response vector and run for 1000 times and draw a distribution. But now, I get r2 values highest 98% and some of them more than 70 %. Is it expected from such type of test? *I was under impression that, r2 with real data set

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

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

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

Hi All, I am trying to fit a piecewise lasso regression, but package Segmented does not work with Lars objects. Does any know of any package or implementation of piecewise lasso regression? Thanks, Lucas