similar to: envelopes of simulations

Dear all, Does any one knows why should I get the following error message, when trying to do a simple data.frame?? DataF<-data.frame(Subject,BiomR,Spp,Capas,Litter,Herbs,LitterD,MaxCanH,DDifS p,DSSp,Slope, CanDens,NearestSp) Erro em data.frame(Subject, BiomR, Spp, Capas, Litter, Herbs, LitterD, : arguments imply differing number of rows: 202, 0 The data I am using

Hi I looked, but I didn't find it: I need an array [0 to 101] where each element is a list (the result of Kest in spatstat). I.e. I want to do: A[2] <- Kest(pp1) A[3] <- Kest(pp2) ... A[101] <- Kest(pp100) How can I create A ? Rainer -- Rainer M. Krug, Dipl. Phys. (Germany), MSc Conservation Biology (UCT) Department of Conservation Ecology University of Stellenbosch Matieland

Dear all, I am using the spatstat package, in particular the Jest, Jdot and Jcross functions. When I plot the results using J <- Jest (SpatData) plot.fv (J, main="Recruits") there are 4 different lines (different colours and shapes): black _____ green -------- red _ _ _ _ blue _._._ Could someone please tell me which line is which?? Regards, Sara Mouro [[alternative HTML

Hi I'm working with the function Kest in the package SpatStat (under LINUX with R 2.1.0). In order to evaluate the statistical significance of my point pattern I'm doing 999 Montecarlo replications. The script that use the Kest function runs OK for most of the different point patterns that I have but for a particular point pattern, which have only 17 points, it runs until the 34th

Hello, I am writing cause I have a problem when try to create confidence envelopes in spatstat. I have a point data representing firms in my study area. When I try to create confidence envelopes the error message turns up: [> p <- envelope(point, fun=Kest, nsim=99) Error in marks.ppp(Y, dfok = FALSE) : Sorry, not implemented when the marks are a data frame] Could you write me how can I

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() }

Hi--I'm trying to run a Ripley's K analysis on a point pattern within a window of 1 square km. The maximum distance I want to use is the diagonal of the window, around 1400m, run in 50m increments. The function defaults to a 250m maximum--I thought this was simply the way I had plotted it, but when I exported the data there was nothing over 250m. I've looked into seq and ginterval,

Dear Developers, Having spent time developing and thinking about how data aggregation and summary statistics can be enhanced in R, I would like to present my ideas/efforts in the form of two commands: The first, which for now I called 'collap', is an upgrade of aggregate that accommodates and extends the functionality of aggregate in various respects, most importantly to work with

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

hi! I did Riply's K function and envelope in the package "SPATSTAT". When the species number is less, it can work well and it is quickly. But when the species number is more(example:2000), it told me"memory limit". So I change "memory limit". But the speed is very slow. I took 13 hours to run the programe. Perhaps there are other methods that can do it. Or just

Hi All! As a new useR, I am currently working in R 2.5.1 to produce Ripley's L (the linear version of Ripley's K) for a spatial point dataset (originally a ESRI shapefile). I have coerced the data to a ppp object (because I was originally using 'kest' in the spstat package to produced Ripley's K) but I need to convert my spatial data to a spp object to be able to use

Hi: I have a query related to the J and Jcross functions in the SpatStat package. I use J to finding indications of clustering in my data, and Jcross to look for dependence between point patterns. I use the envelope function to do Monte Carlo tests to look for significance. So far so good. My question is how I can test to see if tests are significantly different. For example, if find J of

Dear R-helpers, After putting my somewhat sparse (21 points) data into a 'ppp' format, I am unable to calculate the K function using Kest. I get the error "some 'x' not counted; maybe 'breaks' do not span range of 'x' ". So I tried to calculate the K function using the swedishpines dataset. Of course Kest(swedishpines) works fine but when i break it

Hi R users, This query is regarding the use of the 'envelope' function in Spatstat. My data can be represented as a point process with CONTINUOUS marks: points <- ppp(x=x,y=y, marks=m, window= wind) However the marks are alignments (lines), and so have to be treated differently to normal scalar marks. Hence to create a mcf object with the appropriate test function for alignment

Hi, R-users, I met a problem: Items:[Anna 'moi =) akku loppu joskus 4ltä. Kestää kauan nää..'\tAmer, Tuusula (0:20)\t20\t12\t16\t00\t00\t11]/Anne 'Ei jakoa,uus päivä muistio et 4n niin peruin. Hups'\t (0:16)\t0\t12\t18\t00\t00\t11/Elina 'Konsertissa. En tod. vastaa teille'\tEtu-Töölö, Helsinki (2:40)\t24\t12\t18\t00\t00\t11 I want to parsing the above data into the

Dear Sebastian, Initially I was a bit hesitant to think about yet another way to summarize data, but your illustrations convinced me this is actually a great addition to the toolset currently available in different R packages. Many of us have written custom functions to get the required tables for specific data sets, but this would reduce that effort to simply using the right collap() call. Like

On 26/02/2019 8:25 a.m., Sebastian Martin Krantz wrote: > Dear Developers, > > Having spent time developing and thinking about how data aggregation and > summary statistics can be enhanced in R, I would like to present my > ideas/efforts in the form of two commands: > > The first, which for now I called 'collap', is an upgrade of aggregate that > accommodates and

I have spatial data on various species of trees within plots. What I'd like to know is if their patterns are positively associated or not. Could I use cor.test on the values generated by Kest or Gest functions? or would it be more powerful to do a G i to j and then G j to i analysis instead? Thanks, Suzie __________________________________________________________________ Switch to Netscape

Hi, I am trying to test the random labeling hypothesis with my data. I have a point pattern of bird nests and each nest has a mark that it is either predated or not-predated. I want to test the null hypothesis that the K distribution of predation events is equal to the K distribution of all events (nests). This is the code I am using: Kdif <- function(X,...,i) { Kidot <-

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