similar to: LARS for generalized linear models

Hello list, I've been searching around trying to find whether somebody has written such a package of least angle regression on generalized linear models, like what Lasso2 package does. The extension to generalized linear models is briefly discussed in the comment by D. Madigan and G. Ridgeway. Is such a package available? Thanks, Mike [[alternative HTML version deleted]]

There may be benefits to having a machine learning method that explicitly targets covariate balance. We have experimented with optimizing the weights directly to obtain the best covariate balance, but got some strange solutions for simple cases that made us wary of such methods. Machine learning methods that yield calibrated probability estimates should do well (e.g. those that optimize the

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

Dear R community: My platform: R 1.7.0 + windows2000. I am trying to install the package "lasso2" which I saw in the following web address: http://cran.us.r-project.org/src/contrib/PACKAGES.html#emplik. However, I failed to install it from R menu "Packages| Install package(s) from CRAN" since I could not find this item in the list. Thanks in advance! Rui [[alternative

Dear R community: My platform: R 1.7.0 + windows2000. I am trying to install the package "lasso2" which I saw in the following web address: http://cran.us.r-project.org/src/contrib/PACKAGES.html#emplik. However, I failed to install it from R menu "Packages| Install package(s) from CRAN" since I could not find this item in the list. Thanks in advance! Rui [[alternative

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, I want to know some details about the c routine “lasso” in the functions of “gl1ce()” . However, I have following troubles. First, I can not find the routine in the local directories of this function (or package). Second, if I found the routine, could I call it just like this way, say, fit <- .C("lasso", …,PACKAGE = "lasso2") in my own functions. My system is

I have had problems like this before. Probably there is something important that I don't know about routing. Let me introduce to you "Lasso2", a CentOS 4 www server that has been working perfectly well for years. Now I added a second nw card (eth1), automatically using kudzu. I cannot get this dual nw setup to work. The first nw card (eth0) stopped at once working properly,

There are some googlable ways to feed a list of filenames to vim, but I stumble on weird results. With my filelist, I try to do cat list | xargs vim ...to edit the files listed in the file "list". Here's what happens: [root at lasso2 tempdir]# ls -l total 8 -rw-r--r-- 1 root root 0 May 17 18:28 a -rw-r--r-- 1 root root 0 May 17 18:28 b -rw-r--r-- 1 root root 3 May 17

Dear all, I am quite puzzled about the bound and absolute.t arguments to the l1ce function in the lasso2 package. (The l1ce function estimates the regression parameter b in a regression model y=Xb+e subject to the constraint that |b|<t for some value t). The doc says: bound numeric, either a single number or a vector: the constraint(s) that is/are put onto the L1 norm of the parameters.

Dear all, I was recently contacted by a user about an alledged problem/bug in the latest version of lasso2. After some investigation, we found out that it was a user error which boils down to the following: > x <- matrix(rnorm(200), ncol=2) > var <- "fred" > apply(x, 2, var) Error in get(x, envir, mode, inherits) : variable "fred" of mode "function"

Dear R-help, I am having trouble with the predict function in lasso2. For example: > data(Iowa) > l1c.I <- l1ce(Yield ~ ., Iowa, bound = 10, absolute.t=TRUE) > predict (l1c.I) # this works is fine > predict (l1c.I,Iowa) Error in eval(exper,envir, enclos) : couldn't find function "Yield" And I have similar trouble whenever I use the newdata argument in

Dear R-users, For the purpose of model selection I am looking for a way to exhaustively (and efficiently) search for best subsets of predictor variables for a logistic regression model. I am looking for something like leaps() but that works with glm. Any feedback highly appreciated. -- Harald von Waldow <hvwaldow at chem.ethz.ch> Safety and Environmental Technology Group Institute for

Dear all , Is there any subset regression (subset selection regression) package in R other than "leaps"? Thanks and regards Alex [[alternative HTML version deleted]]

Hi, First, when I try the example Prostate with bound 0.44 (as in the manual), I got a different result: > l1c.P <- l1ce(lpsa ~ ., Prostate, bound=0.44) > l1c.P .... Coefficients: (Intercept) lcavol lweight age lbph svi 1.0435803 0.4740831 0.1953156 0.0000000 0.0000000 0.3758199 lcp gleason pgg45 0.0000000 0.0000000

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

Is it possible to use the '\ell' (i.e. the log likelihood) in plots? I've been browsing the plotmath documentation unsucesfully. Cheers, Mario dos Reis mdosrei at nimr.mrc.ac.uk +44 (0)20 8816 2300 Division of Mathematical Biology National Institute for Medical Research The Ridgeway Mill Hill London, NW7 1AA, UK

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, Dear Greg, I AM A NEW to GBM package. Can boosting decision tree be implemented in 'gbm' package? Or 'gbm' can only be used for regression? IF can, DO I need to combine the rpart and gbm command? Thanks so much! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

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