similar to: color quantization / binning a variable into levels

Hi, I have an array of 2d node locations and an array triangles, and would like to plot something like a image or persp. An example of doing it with rgl is: library(ncdf) library(rgl) # wget http://www.maplepark.com/~drf5n/extras/teapot.nc teapot<-open.ncdf("teapot.nc") z<-get.var.ncdf(teapot,"tris") l<-get.var.ncdf(teapot,"locations")

Hi, I'd like to partition a 2d probability density function into regions of equal probability. It is straightforward in the 1d case, like qnorm(seq(0,1,length=5)) but for 2d I'd need more constraints. Any suggestions for how to approach this? Is seems like a spatial sampling problem but I'm not sure where to look. Thanks for your time, Dave -- Dr. David Forrest drf at

Hi, According to help(sub), the ^ should match the zero-length string at the beginning of a string: sub('^','var',1:3) # "1" "2" "3" sub('$','var',1:3) # "1var" "2var" "3var" # This generates what I expected from the first case: sub('^.','var',11:13) # "var1" "var2"

Hi, I've estimated a simple kernel density of a univariate variable with density(), but after I would like to find out the CDF at specific values. How can I do it? thanks for your help, with it I am very close to finish my first little bit more serious work in R, Viktor

I don't want to gripe here, but I would like to know the best method for submitting suggestions and patches to the documentation. It looks like for many help items, we need to track from the html file which help.search("*")/help("*") presents, back to the authoritiative documentation file from which they are generated. It seems the installation process discards much of

Hi All, I'd like to plot something like http://www.nawcwpns.navy.mil/~weather/mugu/mesodata/analysis.html Looking through the galleries at http://addictedtor.free.fr/graphiques/allgraph.php http://r-spatial.sourceforge.net/gallery/ http://fawn.unibw-hamburg.de/cgi-bin/Rwiki.pl?GraphGallery demo(graphics) I did not find a function to plot a 2d field on a matrix. I did find mention of a

Hi, I'd like to produce a marginless or zero margin plot so that the pixel coordinates represent the mathematics. xy<-data.frame(x=c(0,1,1,0,0),y=c(0,1,0,0,1)) png('junk.png',width=300,height=300) par(mar=c(0,0,0,0)) plot(xy$x,xy$y,xlim=c(0,1),ylim=c(,1)) dev.off() The resultant file has about a 10 pixel margin around these lines, and I'm not sure what parameter or function

I have written the following function to calculate the weighted mean difference for univariate data (see http://www.xycoon.com/gini_mean_difference.htm for a related formula). Unsurprisingly, the function is slow (compared to sd or mad) for long vectors. I wonder if there's a way to make the function faster, short of creating an external C function. Thanks very much for your advice. gmd

Hi All In trying to correlate some tide gauge data I need to deal with varying timezones. From the documentation on strptime, it seemed that the tz variable might have some effect on the conversion, but I'm not seeing an effect. > strptime("20061201 1:02 PST",format="%Y%m%d %H:%M",tz="PST")+0 [1] "2006-12-01 01:02:00 EST" >

I have some disconnected boundary data from a finite element ocean model and I'd like to make a plot. Maptools looks promising, but since my data is not in a shapefile or a map, I'm unclear on what the best way to approach the problem. >geom[1:10,] lon lat depth 1 -75.42481 35.58192 16.172 2 -75.40726 35.58567 18.045 3 -75.41351 35.60312 17.333 4 -75.38888 35.58959

In the document "R Data Import/Export", section "Output to connections", there is the following portion of code: ## convert decimal point to comma in output, using a pipe (Unix) zz <- pipe(paste("sed s/\\./,/ >", "outfile"), "w") cat(format(round(rnorm(100), 4)), sep = "\n", file = zz) close(zz) ## now look at the output

Hi R fans, As a newbie following the five-hour rule (after hitting my head against the wall for five hours, post to this list), I am appealing for some help understanding geom_rect() in ggplot2. What I want to do is very simple. I want to generate a plot of rectangles. Each one represents a business cycle. The x-values will be pairs representing the start and end of each cycle. The y-values

Suppose I have x<-data.frame(v1=1:4, v2=c(2,4,NA,7), v3=rep(1,4), v4=LETTERS[1:4],v5=rep('Z',4)) or a much larger frame, and I wish to test for and remove the constant numeric columns. I made: is.constant<-function(x){identical(min(x),max(x))} and apply(x,2,is.constant) # Works for numerics x[,-which(apply(x,2,is.constant))] I'd really like to be able to

There seems to exist peculiar cases where optim does not take care of constraints on the parameters to be optimized over. The call to optim is of the form opt <- optim(cp, fn=sn.dev, gr=sn.dev.gh, method="L-BFGS-B", lower=c(-Inf, 1e-10, -0.99527), upper=c( Inf, Inf, 0.99527), control=control, X=X, y=y, hessian=FALSE) The code has worked fine

Dear R-help list members, I have created a package 'mnormt' with facilities for the multivariate normal and t distributions. The core part is simply an interface to Fortran routines by Alan Genz for computing the integral of two densities over rectangular regions, using an adaptive integration method. Other R functions compute densities and generate random numbers. The starting

I have justed upgraded R v1.7.0 on Windows NT 4 and have installed the latest RMySQL (version 0.5-1)and DBI (version 0.1-5) packages. When I issue the following commands (tactfully adjusted) R just crashes and disappears, any ideas? require(RMySQL) m <- dbDriver("MySQL") con <- dbConnect(m, dbname="xxx", user="xxx", password="xxx",

Hello, How can I obtain the likelihood ratio of a Poisson regression model? Regards. _____________________________________________ dr. Marziliano Ciro Facolta' di Economia Universita' degli Studi di L'Aquila p.zza del Santuario, 19 67040 Roio Poggio, L'Aquila tel.: 0862 434836 fax: 0862 434803

I am learning glm function, but how do you fit data using exponential distribution with glm? In the help file, under "Family Objects for Models", no ready made option seems available for the distribution as well as for other distributions satisfying GLM requirements not listed there.

Dear list, Does anyone know how to fit the power law distribution? I have the empirical distribution and would like to check whether it fits the power law (with the power estimated from the data). Any hints are appreciated. Tanks a lot! Galina [[alternative HTML version deleted]]

Hello... Is it possible to use "density" or another kernel density estimator to identify the mode of a distribution? When I use 'density', the resulting density plot of my data is much cleaner than the original noisy histogram, and I can clearly see the signal that I am interested in. E.g., suppose my data is actually drawn from two or more normal (or other)