similar to: Quadrat Variance analysis

I have photographs of plots that look like so: http://r.789695.n4.nabble.com/file/n4631960/Untitled.jpg I need to divide it up so each circle has an equal area surrounding it. So into 20 equal segments, each of which contains a circle. Quadratcount is not sufficient because if I divide it up into 36 equal quadrats, some quadrats do not contain one of the circles. I'm not even sure how to

Hi, I know there are older threads discussing the quadratcount function in spatstat. Unfortunately, I could not find a solution to my problem there. I'm analyzing a point pattern in an irregular polygonal window. Both the window (an entire country) and the points are projected using WGS84. When I do quadratcount with only one quadrat for the entire country it holds all my 154 points:

Hi all, I am using quadratcount in spatstat to divide a window containing a point pattern into a grid of quadrats containing the intensity of points in each quadrat. My data is in UTM co-ordinates. My window is defined as follows: >p15<-ppp(x,y,window=owin(c(341710,342100),c(3126465,3126780)),marks=NUL L, checks=TRUE) Giving me a distance of 390m in the 'x' direction and 315m in

hello there, Is there a way of truncating in the opposite direction so as to retain only the values to the right of the decimal?? i.e. rather than: > trunc(39.5) [1] 39 i would get something like: > revtrunc(39.5) [1] 0.5 I've been searching to no avail but I imagine there is a very simple solution! Tyler -- View this message in context:

Hello, I am trying to plot a quadratcount object over a ppp object in the spatstat package. I ultimately want to get something like this http://bg9.imslab.co.jp/Rhelp/R-2.4.0/src/library/spatstat/man/images/big_quadratcount_001.png http://bg9.imslab.co.jp/Rhelp/R-2.4.0/src/library/spatstat/man/images/big_quadratcount_001.png See

I have a function (see below). This function has one object, ID. If I run the loops by themselves using a character value (ie,"VFFF1-7") instead of the function object, then the loops work fine. However, when I try to insert the character value via the function call, it doesn't work. I don't get an error, but the TotalCover.df dataframe does not update according to the loop

I have been looking into both of these approaches to conducting a GLMM, and want to make sure I understand model specification in each. In particular - after looking at Bates' Rnews article and searching through the help archives, I am unclear on the specification of nested factors in lmer. Do the following statements specify the same mode within each approach? m1 = glmmPQL(RICH ~ ZONE,

I have a function (see below). This function has one object, ID. If I run the loops by itself using a character value (ie,"VFFF1-7"), then the loops work fine. However, when I try to insert the character value via the function call, it doesn't work. I don't get an error, but the TotalCover.df dataframe does not update according to the loop criteria. Any obvious problems that

Hello! I want to plot a multitype point pattern called "new" in package spatstat. When I write plot(new) in the graphic window I can see a strech rectangle with a point inside, not the point pattern.If I write plot(new$x,new$y) the point pattern is plot ok.The problem is that I want do the density plots, quadrat count, etc and in these cases I can?t write density(new$x,new$y), I

I have numerous objects, each containing continuous data representing the same variable, movement rate, yet each having a different number of rows. e.g. d1<-as.matrix(rnorm(5)) d2<-as.matrix(rnorm(3)) d3<-as.matrix(rnorm(6)) How can I merge these three columns side-by-side in order to create a table regardless of the difference in length? I wish to analyze the output in a spreadsheet

I have data on the proportion of clutches experiencing different fates (e.g., 4 different sources of mortality) for 5 months . I need to test 1) if the overall proportion of these different fates is different over the entire study and 2) to see if there are monthly differences within (and among) fate types. Thus, I am pretty sure this is an RM analysis -( I measure the same quadrats each month).

Dear list members, I'd like to perform a glm analysis with a hierarchically nested design. In particular, I have one fixed factor ("Land Use Classes") with three levels and a random factor ("quadrat") nested within Land Use Classes with different levels per classes (class artificial = 1 quadrat; class crops = 67 quadrats; and class seminatural = 30 quadrats). I have four

Hi, With the following code i can divides window into quadrats and counts the numbers of points in each quadrat. library(spatstat) X <- runifpoint(50) quadratcount(X) quadratcount(X, 4, 5) quadratcount(X, xbreaks=c(0, 0.3, 1), ybreaks=c(0, 0.4, 0.8, 1)) qX <-? quadratcount(X, 4, 5) plot(X) plot(qX, add=TRUE) But I want to mark each? quadrats? and select/ extract only those points by

Dear list members, First of all thank you for your helpful advices. After your answeres to my firt mail I studied a lot (R-News n?5) and I tried to perform my analysis: First, to fit a GLM with a nested design I decided to use the function "lmer" in package "lme4" as suggested by Spencer Graves and Filippo Piro. I remember to you that my data were: land use classes, 3 levels

I am trying to set up a function which processes my data according to the following rules: 1. if (x[i]==0) NA 2. if (x[i]>0) log(x[i]/(number of consecutive zeros immediately preceding it +1)) The data this will apply to include a variety of whole numbers not limited to 1 & 0, a number of which may appear consecutively and not separated by zeros. Below is an example with a detailed

I'm using R for Mac OSX version 1.14 (2129) and the geoR package version 1.6-5 (the current version in the R repository). I'm running R in OS 10.4.6 on a Mac G4 iBook (933MHz, 640 MB DDR SDRAM). I searched the R archive and did not find a posting on this issue. I want to use the variog and variog4 functions of geoR to characterize the pattern of spatial autocorrelation of tree

Hi there, I'm dealing with a pretty big dataset (~22,000 entries) with numerous entries for every day over a period of several years. I have a column "judy" (for Julian Day) with 0 beginning on Jan. 1st of every new year (I want to compare tendencies between years). However, in order to control for a leap year (2004), I simply need to subtract 1 from every judy value for the year

Is there any way to identify or infer the inflection points in a smooth spline object? I am doing a comparison of various methods of time-series analysis (polynomial regression, spline smoothing, recursive partitioning) and I am specifically interested in obtaining the julian dates associated with the inflection points inferred by the various models. Tyler e.g.

I'm trying out a basic plot, but something about the way I subset my data leads to problems with the plot. Here is the first bit of my data set year,date,location,quadrat_juvenile,photo_location,photo_exists,genus,count,divers 2005,2005-04-30, 1 Fringing Reef,1, 1 Fringing Reef Coral Transect Pole 1-2 Quadrat 1,t,Acanthastrea,0,HP+MEM 2005,2005-04-30, 1 Fringing Reef,1, 1 Fringing Reef

Dear R-helpers, Apologies in advance for this (probably) simple question. I've searched the R Archive and can't seem to find a solution to my problem. I have a data frame of vegetation quadrat data with the following format: Q S C 1 A 5 1 B 10 1 C 50 1 D 10 2 A 20 2 E 10 2 C 40 3 D 5 3 F 1 3 G 5 3 B 75 Where Q is the sample (vegetation quadrats), S is the species and C is the