similar to: Questions regarding use of predict() with glmpath

We have uploaded to CRAN a new version of glmpath, a package which fits the L1 regularization path for generalized linear models. The revision includes: - coxpath, a function for fitting the L1-regularization path for the Cox ph model; - bootstrap functions for analyzing sparse solutions; - the ability to mix in L2 regularization along with L1 (elasticnet). We have also completed a report that

We have uploaded to CRAN a new version of glmpath, a package which fits the L1 regularization path for generalized linear models. The revision includes: - coxpath, a function for fitting the L1-regularization path for the Cox ph model; - bootstrap functions for analyzing sparse solutions; - the ability to mix in L2 regularization along with L1 (elasticnet). We have also completed a report that

The problem first appeared in R 2.6.1 and is still there in R 2.6.2 On Windows running R CMD check command for glmpath package fails. The reason seems to be that when R is running the examples file (glmpath-Ex.R), it skips about 50 lines and as a result gives a syntax error. I'm working with a modified version of the CRAN glmpath 0.94. My version happens to give a more clear example of a

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the

Hi, perhaps you can help me to find out, how to find the best Lambda in a LASSO-model. I have a feature selection problem with 150 proteins potentially predicting Cancer or Noncancer. With a lasso model fit.glm <- glmpath(x=as.matrix(X), y=target, family="binomial") (target is 0, 1 <- Cancer non cancer, X the proteins, numerical in expression), I get following path (PICTURE

Thanks for the reproducable example. I can confirm that it fails on my machine using survival 2-37.5, the next soon-to-be-released version, The issue is with NextMethod, and my assumption that the called routine inherited everything from the parent, including the environment chain. A simple test this AM showed me that the assumption is false. It might have been true for Splus. Working this

Dear R- helpers Following a draft structure of the R script for which I am facing problem Step 1 x <- of type array with original values y <- of type array with original values Step 2 for (ctr in 1:10) { # my problem here the both x and y still show the original values from step 1 # in spite of making changes to the old values of the arrays x and y in the function function

In one particular situation predict.coxph gives an error message. Namely: stratified data, predict='expected', new data, se=TRUE. I think I found the error but I'll leave that to you to decide. Thanks, Chris ######## CODE library(survival) set.seed(20121221) nn <- 10 # sample size in each group lambda0 <- 0.1 # event rate in group 0 lambda1 <- 0.2 # event rate in group 1

Hi, I am new to model selection by coefficient shrinkage method such as lasso. And I became particularly interested in variable selection in Cox regression by lasso. I became aware of the coxpath() in R package glmpath does lasso on Cox model. I have tried the sample script on the help page of coxpath(), but I have difficult time understanding the output. Therefore, I would greatly appreciate if

Is there a way to make a shading interpolation on an image plot? Something similar to matlab 'shading interp', I think it is called Gouraud shading. What I want is to make a image plot look nicer. with image() it looks very facetted, and I would like to make it look smoother. I've tried with interp.surface() in fields package but it (obviously) makes nan values at the borders and

Hallo, I have a set of S4 and S3 classes together in one script. While running this script I create a lot of new functions and objects An example for S3 and S4 classes: ## S3 classes pt <- list(x=1,y=2) class(pt) <- "xypoint" xpos <- function(x, ...) UseMethod("xpos") xpos.xypoint <- function(x) x$x ypos <- function(x, ...) UseMethod("ypos")

Hi - When using the randomForest function for regression, I get different results for mean-squared error of the predictions depending on whether or not I specify to calculate variable importance. There is an example below. I looked briefly at the source code, but couldn't find anything that would indicate why calculating variable importance would (or should) change predictions. I'm

Are the results dramatically different? The result would be expected to be somewhat different, as setting importance=TRUE would make many calls to the random number generator (for permuting OOB data in each variable), making all but the first tree in the forest different than if importance=FALSE. Cheers, Andy > From: Scott Gilpin > > Hi - > > When using the randomForest

Hi all, I am using glampath package for L1 regularized logistic regression. I have read the article " L1 regularization path algorithm for GLM" by park and Hastie (2006). One thing I can't understand that how to find best lambda for my prediction. I want to use that lambda for the prediction not the entire set. thanks. -- View this message in context:

Dear R users I need to auto scale the left y axis in the code below, so that when I scroll left or right the left y-axis scale changes to accumulate the range of the displayed data with in the max hight of the y-axis. also how can I make the crosshair horizontal since it is only vertical in this code. this code with a kind help from "Gregory (Greg) L. Snow Ph.D." just

Please see the code below. When I try to run the variogram - vg.deft<-variog(rd,uvec=10) I keep getting this error- variog: computing omnidirectional variogram Error in vector("double", length) : vector size specified is too large Also, when I try to define distance-based neighborhood - nb.tr=dist.neighbors(tr.locs,2) I get this error - Error in vector("double", length) :

Hi thank you for talking the time to help me with this. I have a sequence of numbers in a file and an equal sequence of various character, say(a b c d) each occurs more than once. I need to plot the numbers so that numbers corresponding to a in the other sequence would have green dots, those corresponding to b a red dot, nothing on c and blue square for d. i.e 2 a show a green dot 4 b show a

Colleagues, I am using "nls" successfully (2.11.1, OS X) but I am having difficulties retrieving part of the output - residual sum of squares. I have assigned the output to FIT: > > FIT > Nonlinear regression model > model: NEWY ~ PMESOR + PAMPLITUDE * cos(2 * pi * (NEWX - POFFSET)/PERIOD) > data: parent.frame() > PMESOR PAMPLITUDE POFFSET >

Hi, I have a data.frame that I need to construct iteratively. At the moment, I'm doing: d<-data.frame(x=c(),y=c(),z=()); # {and, within some loop} d<-rbind(d,data.frame(x=newx,y=newy,z=newz); While this works, it is horribly verbose and probably not efficient, either. My real data.frame has, of course, many more columns, which can be of different modes. I vaguely recall that