similar to: sampling from the uniform distribution over a convex hull

Dear UseRs, I would like to know which function is the most efficient in finding convex hull of points in 3(or 2)-dimensional case? Functions for finding convex hull is the following: convex.hull (tripack), chull (grDevices), in.chull (sgeostat), convhulln (geometry), convexhull.xy (spatstat), calcConvexHull (PBSmapping). I also would like to know if there is a function that can be used

Dear list, Does anyone have a suggestion (or better still) code for sampling from the uniform distribution over the convex hull of a set of points? Many thanks and best wishes, Ranjan

Hi everyone, I wonder if there is a code in r that can generate a 3d convex hull from a data-frame containing 3 columns and then use another database with the same three columns and for each row determine if the xyz point is inside or not the convex hull generated with the first database? The package geometry allows to calculate a hull and it's volume. I was planning to calculate the volume

Dear R gurus and users, I seem to have problem finding the right tool for plotting convex hulls over 2D plots, after a cluster analysis. In fact I would like to draw a convex hull in 2D for a generic group of points. I found a "convhulln", but this doesn't seem to give me a convex hull. Here is what I do: > library(mvtnorm) > Mean <- c(2,1) > Sigma <-

Hi there, I have been using the Tps function (within the Fields package) for a while now to interpolate different sedimentary units. Due to the method of formation of the units I know that at some edges the thickness of the unit decreases to zero. I was wondering if there was someway to specify that the interpolation only occurs within the convex hull of the data, outside of which the the values

Does anyone know of a R package that will determine the convex hull of a high-dimensional dataset (say 4-10 dimensions). I know chull works for 2D data. I'm neophyte to R and convex hulls so please keep it simple. Many thanks Ben -- Ben Stapley. Biomolecular Sciences, UMIST, PO Box 88, Manchester M60 1QD. Tel 0161 200 5818 Fax 0161 236 0409

Dear List Members, I'm presently carrying out morphological analysis of a data set of neuronal structures. These are essentially 3D binary trees. In due course I will be trying to use discriminant analysis or other methods to classify these neurons based on morphological variables such as total tree length, segment number etc. I would like to calculate a 3D convex hull for a set of X,Y,Z

Hi, I have a problem while loading the "siar" program in R. When I am loading siar, system does not load convexhull. On the screen I have seen such writings. The following object(s) are masked from ‘package:spatstat’: convexhull How can I load the convexhull, how can I unmask from this package? I will be appreciated if you give advice about this. Best Sukran

Does anybody know if there are functions to: 1. Define a convex hull on a space with more than 2 dimensions? chull just works for a plane. 2. Numerically select elements within a given complex hull. Thanks! Dr. Agustin Lobo Instituto de Ciencias de la Tierra (CSIC) Lluis Sole Sabaris s/n 08028 Barcelona SPAIN tel 34 93409 5410 fax 34 93411 0012 alobo at ija.csic.es

Hello everybody-- I have been playing with my data in spatstat, and what I'd like to present is a basic exploratory spatial analysis. I have used the following code, using a ppp.object called tsdspoints. The code develops the simulations and the envelopes I want, but I don't understand my first plot here, the [tsds.ghat.short$r, tsds.ghat.short$raw]...I cobbled together this code

I've written the function below to simulate the mean 1st through nth nearest neighbor distances for a random spatial pattern using the functions nndist() and runifpoint() from spatsat. It works, but runs relatively slowly - would appreciate suggestions on how to speed up this function. Thanks. --Dale library(spatstat) sim.nth.mdist <- function(nth,nsim) { D <- matrix(ncol=nth,

Hi I have the following loop and would like to vectorize it. Any ideas if it is possible? Thanks, Rainer Tha Loop: for (i in 2:Result$NoSims) { ppp <- runifpoint(Result$NoPlants) K <- Kest(ppp) Result$LSim[i,] <- sqrt(K$iso / pi) - K$r CM <- (Result$LSim[i,] * Result$LSim[i,]) / abs(K$r[2] - K$r[1]) Result$SigCM[i] <- sum(CM, na.rm=TRUE) print(i) flush.console() }

I am modeling a trend surface using trmat and want to trim the resulting matrix to the area enclosed by my real data (i.e., remove all the extrapolated areas). I was using chull and in.chull to calculate the convex hull and change all the other values created by trmat to NA. However, my real data has portions that are slightly concave so chull would give me slivers that are extrapolations from

I've been asked, and interested myself: Has anybody implemented the above in R or another S language dialect? We are talking about the algorithms / methodology by Wellner, Groeneboom and Jongbloed, e.g., from the following article @Article{Jongbloed:1998:ICM, author = "Geurt Jongbloed", title = "The Iterative Convex Minorant Algorithm for

Hello, What are the minimum number of points in a point pattern before a clustering analysis using a Ripley K function loses any meaning? I haven't been able to find much comment in the literature about this --- instead case examples typically use 100-200 points as a minimum. Regards, Sebastian Pucilowski

Dear R Users, We are pleased to announce the first release of CVXR which implements Disciplined Convex Programming (DCP) in R on CRAN. Like CVXPY in Python, CVXR provides a domain specific language for formulating convexoptimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical

Dear R Users, We are pleased to announce the first release of CVXR which implements Disciplined Convex Programming (DCP) in R on CRAN. Like CVXPY in Python, CVXR provides a domain specific language for formulating convexoptimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical

Hi I have a loop which is doing time consuming calculations and I would like to be able to have some feedback on where it is in it's calculations. I tried to simply show the counter variable in the loop, but id doesn't work as all display seems to be delayed until the loop is completed. Is there any way of displaying the progress of a loop? Rainer The loop: for (i in

Recently, a package for convex optimization was announced for Python, based upon the LP solver GLPK, the SDP solver in DSDP5, and the LP and QP solvers in MOSEK. I'm aware GLPK is available for R, but wondered if anyone had good packages for convex optimization along these lines for R. TIA.

Can anyone suggest a simple way to calculate a 'hash code' from an arbitrary R object? hash(x) should return an integer or string with the property that if hash(x) != hash(y) then x and y are not identical and the time to compute hash(x) should be quite short. Any suggestions welcome thanks Adrian Baddeley