similar to: Dual nw card problem again

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 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

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"

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"] <-

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

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

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]]

Hi, Is there an R implementation of least angle regression for binary response modeling? I know that this question has been asked before, and I am also aware of the "lasso2" package, but that only implements an L1 penalty, i.e. the Lasso approach. Madigan and Ridgeway in their discussion of Efron et al (2004) describe a LARS-type algorithm for generalized linear models. Has

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

Hello, I am trying to shrink the coefficients of a logistic regression for a sparse dataset, I am using the lasso (lasso2) and I am trying to determine the shrinkinage factor by cross-validation. I would like please some of the experts here to tell me whether i'm doing it correctly or not. Below is my dataset and the functions I use w= a b c d e P A 0 0 0 0 0 1 879 1 0 0 0 0 1 3 0 1 0 0 0 7 7

Hello R users, I have been trying to output all my results (text, plots, etc) into the same postscript file as one document, but have been unable to...Can anyone help me improve my code below so that I can accomplish this? Currently I have to output them separately then piece them back together into one document.. Thanks in Advance for any help! options (scipen=999, digits=7)

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, I tried several settings by using the "family=gaussian" in "gl1ce", but none of them works. For the case "glm" can work. Here is the error message I got: > glm(Petal.Width~Sepal.Length+Sepal.Width+Petal.Length ,data=iris,family=gaussian()) > gl1ce(Petal.Width~Sepal.Length+Sepal.Width+Petal.Length ,data=iris,family=gaussian()) Error in eval(expr, envir,

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi, during today I build R 1.5.0-packages on a SuSE 7.3 they normaly should also run on a 7.2 and 8.0. I also compiled the contribute packages It The following packages failed, cause I believe I haven't installed all neccessary includes and libaries: checking for package ipred ...failed! checking for package cramer ...failed! checking for

Dear List, I am looking for a function that will find the best subset of negative binomial models. I have a large data set with 15 variables that I am interested in. I want an easy way to run all possible models and find a subset of the "best" models that I can then look at in more detail. I have found two functions that seem to provide what I am looking for, but am not sure which

All, Could anyone recommend a package that allows the user to constrain the coefficients from a multiple regression equation? I tried using the gl1ce function in lasso2, but couldn't get it to work. I created a contrived example to illustrate my starting point. data(cars) fmla <- formula(dist ~ speed) gl1c.E <- gl1ce(fmla, data = cars) gl1c.E gl1c.E <- gl1ce(fmla, data =

Hi, Is there any facility in R to perform a stepwise process on a model, which will remove any highly-correlated explanatory variables? I am told there is in SPSS. I have a large number of variables (some correlated), which I would like to just chuck in to a model and perform stepwise and see what comes out the other end, to give me an idea perhaps as to which variables I should focus on. Thanks