similar to: How to use c routines in the exiting package?

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,

Hi, I'm using gl1ce with family=binomial like so: >yy succ fail [1,] 76 23 [2,] 32 67 [3,] 56 43 ... [24,] 81 18 >xx c1219 c643 X1 0.04545455 0.64274145 X2 0.17723669 0.90392792 ... X24 0.80629054 0.12239320 >test.gl1ce <- gl1ce(yy ~ xx, family = binomial(link=logit), bound = 0.5 ) or >omit <- c(2,3) >test.gl1ce

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"

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

X is a matrix and F is a vector. F2 <- data.frame(cbind(X,F)) F2 V1 V2 V3 F 1 -0.250536332 -1.4755883 1.9580974 -2.136487 2 -0.009856084 0.4953269 0.5486092 -2.744482 3 -0.406962682 0.7729631 0.1861905 -2.891821 4 1.938780097 0.7469251 1.2537781 -1.212992 5 -0.332370358 1.1943637 0.7114278 -1.830441 modF<-formula(F ~ V1 + V2 + V3) #no error message

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, 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

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

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 folks, I have a question about how to load a dll. First, I use the command > dyn.load("lassofu.dll"); then, I got the message below “NULL Warning message: DLL attempted to change FPU control word from 9001f to 90003”; After I tried to use this dll in one of s functions, I got the message below “Error in .Fortran("lasso", as.double(x), as.double(y), as.double(b),

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

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

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

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 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 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

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]]