similar to: lasso for variable selection

Okay, been working with tripack, seems the most mature package for this. Got it to work well with their test data set - data(tritest). When i tried random numbers to explore further, i am getting some results that don't reconcile. example run this: library(tripack) y <- runif(100) x <- runif(100) vm <- voronoi.mosaic(x,y) plot(vm) par(new=T) plot(x,y,col='blue') when

Dear R-users, Does anyone know how to do weighted Voronoi diagrams (Dirichlet tesselation) in R? To be more specific, I have a set of coordinates for tree locations on a plot, and I'm looking for a way to do the tesselation so that the polygon size for each tree depends on the size of the subject tree, and the size of its neighbors. So, the location of the bisection between two trees

Hi all, Given a number of points (x,y) in a plane, I'd like to plot a map of polygons, so that 1) each polygon contains exactly one point 2) the polygon defines the area for which this specific point is closer than any other point. It's a bit like a map of areas "influenced" by that point, and it's obviously a matter of intersecting the perpendicular bisectors

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

The problem: I would like to translate the Octave algorithm in griddata.m to R. Within the griddata algorithm calls are made to the Delaunay function. For the R translation I have found delaunayn within the "geometry" package and also the deldir package. Both do similar things but give slightly different results depending on the input. The question is, what is making the results for the

Backtrace: ... 0x00007ffff57fc392 in __assert_fail () from /lib64/libc.so.6 0x00007ffff0cf5bec in nvc0_shader_stage (pipe=<optimized out>) at ./nvc0/nvc0_context.h:204 nvc0_set_constant_buffer (pipe=0x631080, shader=<optimized out>, index=<optimized out>, cb=0x0) at nvc0/nvc0_state.c:771 0x00007ffff0a2cf63 in st_destroy_context (st=0x68a9f0) at state_tracker/st_context.c:382

"The question is, what is making the results for the R packages different from each other?" There are literally thousands of R packages, contributed independently by thousands of people. There should be no expectation of consistency or for that matter, "correctness", among them. Caveat emptor. Only within the base R distribution, maintained and mostly written by the R Core

Thanks so much John! That works :) Previously I wrote a pass that inherited from CallGraphWrapperPass and used its getCallGraph() function within runOnModule() which resulted in segmentation faults for me. Not sure why that happened, perhaps callgraph wasn't setup by the time runOnModule() was called internally. If you know why, kindly enlighten me and perhaps document the behavior for the

Hello list, I'm just getting started with using R - I have been trying over the past day or so to work out a method for generating voronoi polygons for PostGIS using SQL. I was able to put together a procedure which works relatively well, but is somewhat inefficient. Someone on the PostGIS list pointed me to the deldir() function in R, for which I can export a text file with x/y coordinates

Hi all, I am trying to install Spatstat on OpenSUSE 11.1. install.packages("spatstat", dependencies = TRUE) fails on the basis of various compiler packages (full message below). I have gcc version 4.3.2, which should include gfortran and g++ - so I'm not sure what to do! Richard * Installing *source* package ?deldir? ... ** libs gfortran

There's Renka's STRIPACK, and TRIPACK, respectively, ACM TOMS Algorithms 772 and 751, and there's the R package "deldir" which does the Delaunay for a plane, but does anyone have or know of the tessellation in R for a sphere? Also, is there a standard indexing scheme for Delaunay facets, and perhaps of edges in such facets? I'd expect that to be a publication reference,

I have an HP Photosmart C3180 All-in-one. As I haven't had any need for color lately, when I last replaced ink, I only replaced the black cartridge. It didn't work, even when printing grayscale-only images, even when telling print-set-up grayscale-only. Is there a way to tell my printer to use only the black ink cartridge. My test image is a handmade postscript file that only use setgray

I'm working with the following code below to generate a how do I set the h,c, and l values such that the significant, positive residuals appear different on a grayscale printer from significant grayscale residuals. The challenge as I see it is that one can only distinguish the positive and negative residuals with the hue/. Varying the chroma and the luminance only affect the

Anyone knows how to convert a deldir$delsgs to a X3D IndexedTriangleSet? Are there already any functions/packages? [[alternative HTML version deleted]]

On Sun, 17 Apr 2016, Michael Hennebry wrote: > On Sun, 17 Apr 2016, Michael Hennebry wrote: > >> I have an HP Photosmart C3180 All-in-one. >> As I haven't had any need for color lately, >> when I last replaced ink, I only replaced the black cartridge. >> It didn't work, even when printing grayscale-only images, >> even when telling print-set-up

Hi, is there a grayscale setting for lattice plots? I like the default color settings. I also like the settings that are available for setting black and white with something like this: --8<---------------cut here---------------start------------->8--- ltheme <- canonical.theme(color = FALSE) ## in-built B&W theme ltheme$strip.background$col <- "transparent" ##

Hi all. It's been a long time since I wrote to this list. Glad to see the R project well and working. I am working with a 3D plot similar to this: http://old.nabble.com/file/p26525177/rgl-device.png rgl-device.png The underlying picture is a JPEG image, loaded with the rimage package and coerced to the matrix. Spheres denote control points, collected from this picture and must be

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