similar to: voronoi

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,

Dear all, I am trying to avoid the warnings produced by: > x <- -2:2 > log(x) [1] NaN NaN -Inf 0.0000000 0.6931472 Warning message: production de NaN in: log(x) I thought that using ifelse would be a solution, but it is not the case: > ifelse(test = x < 0, yes = NaN, no = log(x)) [1] NaN NaN -Inf 0.0000000 0.6931472 Warning message: production

thanks a lot Renaud. but i was interested in Finney's fiducial confidence intervals of LD50 so to obtain comparable results with SPSS. But your reply leads me to the next question: does anybody know what is the best method (asymptotic, bootstrap etc.) for calculating confidence intervals of LD50? i could "get rid" of Finney's fiducial confidence intervals but

hello I make a subroutine that give-me a (mathematical) function in string format. I would like transform this string into function ( R function ). thanks for any tips. cleber #e.g. fun_String = "-100*x1 + 0*x2 + 100*x3" fun <- function(x1,x2,x3){ return( ############ evaluation( fun_String ) ############ ) True String mathematical function :-( :-( > nomes [1]

Hi list, is there any direct way to obtain confidence intervals for the regression slope from lm, predict.lm or the like? (If not, is there any reason? This is also missing in some other statistics softwares, and I thought this would be quite a standard application.) I know that it's easy to implement but it's for explanation to people who faint if they have to do their own programming...

Dear Listers, If I have several glm objects with names glm1, glm2.... and want to apply new data to these objects. Instead of typing "predict(glm1, newdata)..." 100 times, is there way I could do so in a loop? Thank you so much! wensui [[alternative HTML version deleted]]

Hello R users: I am fairly new to R and am trying to figure out how to compute an intraclass correlation (ICC) and/or design effect for binary data? More specifically, I am trying to determine the amount of clustering in a data set - that is, whether certain treatment programs tend to work with more or less severe clients. The outcome variable is dichotomous (low severity / high severity)

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

Hi, I am trying to extract the coefficients matrix from a gls summary. Contrary to the lm function, the command fit$coefficients returns only the estimates of the model, not the whole matrix including the std errors, the t and the p values. example: ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <-

Hi: The gls function I used in my code is the following fm<-gls(y~x,correlation=corARMA(p=2) ) My question is how to extact the AR(2) parameters from "fm". The object "fm" is the following. How can I extract the correlation parameters Phi1 and Phi2 from "fm"? These two parametrs is not in the "coef" componenet of "fm". Thanks a

Dear R users, I've been struggling with the following problem: I want to extract the Wald p-value from an lmer() fit, i.e., consider library(lme4) n <- 120 x1 <- runif(n, -4, 4) x2 <- sample(0:1, n, TRUE) z <- rnorm(n) id <- 1:n N <- sample(20:200, n, TRUE) y <- rbinom(n, N, plogis(0.1 + 0.2 * x1 - 0.5 * x2 + 1.5 * z)) m1 <- lmer(cbind(y, N - y) ~ x1 + x2 + (1 | id),

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

I'm running some GEE models but when I request the summary(pcb.gee) all I get are rows and rows of intercorelations and they fill up the screen buffer so I can not even scroll back to see what else might be in the summary. How do I get the summary function to NOT print the intercorrelations? Thanks, -- Dean Sonneborn Programmer Analyst Department of Public Health Sciences University of

Hello there This is the first time I have used r-help message board so I hope I have got the right address. I am trying to check the residuals of a GLMM model(run using the package lme4). I have been able to check the residiuals of REMLs in lme4 using the following: m1<-lmer(vTotal~Week+fCollar+ (1|fCat), collars) res<-resid(m1) plot(res) qqnorm(res) library(MASS) par(mfrow=c(2,3))

Full_Name: Jonathan Lees Version: 2.0.1 OS: linux-gnu Submission from: (NULL) (152.2.75.65) there is a problem with calculating the convex hull in 2-D interpolation using the codes interp fromt eh akima package: x =c(0.6505304, -1.1821562, -0.2600792, 0.7913716) y = c(1.0424226, 0.1754048, -1.4523334, 0.2349112) z = c(0.000, 3.042, 0.370, 0.122) EX = seq(from=min(x), to=max(x),

Hi, I've been looking for functions that can do delaunay tesselation and generate voronoi cells. I came across deldir and tripack but both seem to be restricted to 2D points. Are there any packages that can do a tesselation in N dimensions? I know that Matlab and Mathematica use the qhull package to provide functions for this. Does anybody know of any R packages that do this (maybe by calling

Hello. I'd like to convert voronois object created by tripack to polygons to use them in GIS(Geographic Information Systems) software. I tried to create voronoi objects by using following code. > library(tripack) > x <- rnorm(10) > y <- rnorm(10) > plot(x,y) > v<-voronoi.mosaic(x,y) > plot(v) But from here, I could not create polygons. Of course, I know

Hi all, I have a dataset in which the output Y is observed on two groups of patients (treatment factor T with 2 levels). Every subject in each group is observed three times (not time points but just technical replication). I am interested in estimating the treatment effect and take into account the fact that I have repeated measurements for every subject. If I do this with repeated measures

Hi all, I am running R 2.5.0 under Windows XP Media Center Edition. Here's a problem that's been stumping me for a few days now, and I can't find anything useful in the archives. I am using voronoi.mosaic (tripack package) to create proximity polygons for a study of vegetation competition and dynamics. The points lists are read in from a file for each plot, then 8 duplicates

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