similar to: nested mixed-effect model: variance components

Dear listers, I need to export a distance matrix in the following format : a 0.0 1.0 0.2 1.0 1.0 b 1.0 0.0 1.0 1.0 1.0 c 0.2 1.0 0.0 1.0 1.0 d 1.0 1.0 1.0 0.0 1.0 e 1.0 1.0 1.0 1.0 0.0 I tried write.matrix() from the MASS library, which gives: a b c d e 0.0 1.0 0.2 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.2 1.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 Does anyone know of an R trick to change

Dear Listers, I emailed the list a few days ago about how to bootstrap a community matrix (species by sites) and get a consensus tree with node support. A friend pointed out that a similar question remained unanswered in 2004. I wish to re-word my question: is anyone aware of a package / method to obtain a majority-rule consensus tree from x distance matrices ? Is anyone using R to generate

Hello all, R just ''stopped working'' on my machine (ibook g4, os 10.3.9). between last time I used it and now, I did not do anything but check emails and browse the internet. when I try to start the GUI, it appears and disappears as rapidly. when I tried to run a session from the terminal, I got the following: mkdir: /tmp/Rtmp590-29493: Permission denied mkdir:

Dear Listers, I am trying to match tables that DO NOT have the same length. The tables result from the function "table()" so they look like this: table 1 2 3 4 3 5 7 table 2 1 2 3 6 4 5 I need the following output: (NOTICE THE ZEROS) 1 2 3 4 table1 0 3 5 7 table2 6 4 5 0 Unfortunately, I was not successful using "match()". Previous postings explain how to do

Dear All, I would like to make partial sums (or means or any other function) of the values in intervals along a sequence (spatial transect) where groups are defined. For instance: habitats<-rep(c("meadow","forest","meadow","pasture"),c(10,5,12,6)) observations<-rpois(length(habitats),2)

Dear listers, a central problem in conservation biology is the selection of sites in reserve network design. many algorithms have been published, and I was wondering any have been implemented in R. I did not seen anything on CRAN or R-help, or on the web in general. Best regards, Eric Eric Pante ---------------------------------------------------------------- College of Charleston, Grice

I have two data sets with locations, X, Y of houses (df.house) and habitats(df.habitat), respectively. In each dataset, there are 3 replicates (Repeat). Because each replicate has different locations of houses and habitats, I would like to plot them in panels. I wrote something like this: mypanel<-function(x,y,subscripts,...){ panel.xyplot(x,y,pch=20) panel.xyplot(

Hello all, Can any of you R gurus help me out? I?m not all that great at stats to begin with, and I?m also learning the R ropes (former SAS user). Here?s what I need help with? I have a nested sample design and ran a nested anova, but I don?t know how to interpret the results habitat (four different types) is nested in site (three types), and site is nested in gear (two types)

Hi, I am new in statistics and i manage to make the linear model analysis but i have some difficulties in explaining the results. Can someone help me explalinig the output of my linear model analysis ? My data are with 2 variables habitat (e,s) and treatment (a,c,p) with multiple trials within. Thank you in advance Call: lm(formula = a$wild ~ a$habitat/a$treatment/a$trial) Residuals: Min

Hi All, I am frustrated by mixed-effects model! I have searched the web for hours, and found lots on the nested anova, but nothing useful on my specific case, which is: a random factor (C) is nested within one of the fixed-factors (A), and a second fixed factor (B) is crossed with the first fixed factor: C/A A B A x B My question: I have a functioning model using the aov command (see

Dear fellow R users, Keywords: Kruskal-Wallis, Post-Hoc, pair-wise comparisons, Nemenyi-Damico-Wolfe-Dunn test, coin package, oneway_test I am using the "oneway_test" function in the R package "coin" and I am obtaining results which I cannot believe are accurate. I do not wish to waste anyone's time and so if the following problem is rather trivial, I apologize, however I

Dear R users, I face to a nested pattern and despite the numerous examples in the help I am still confused. I sampled bugs in different habitats within sites which were within rivers themselves within different regions. The habitat correspond to different substrata (not systematically present in all sites). For rivers and sites, I have environemental variables (e.g. altitude and slope of

The NOAA National Centers for Coastal Ocean Science is hiring a Computational Ecologist, a statistical/computational ecologist with experience fitting advanced spatial models to marine wildlife survey data (e.g., seabirds and marine mammal transects, fisheries trawl surveys) in R and other statistical languages. This is a full-time, long-term stable contract position. We are looking for an

Dear all, I would like to solve a trivial problem (I guess it is) but can't find the right way. Maybe someone can help me ? I've got a table with two factors (station = station ID, buffer = buffer size in meters) and a value for each unique combination of those two factors (S = number of habitats within each buffer around each station) like this: TABLE 1 station buffer S Abaia01 200 2

Hi all - R2.0.1 on OSX;MASS library;nlme library I am trying to emulate the solution to a problem set that has normally been run in Genstat, using R. The problem that I am having at the moment is with the following glmm question (using glmmPQL from the MASS library): "We have two different forest habitats (first rotation thicket, and high forest) which we want to survey for the

Hi all, This is as much as statistical/estimation question as an R-specific one, but here goes. I am trying to use logistic regression to predict suitability of habitats for certain plant species. The response variable is a binary one that indicates whether a particular species is found at a site on the landscape. The independent variables represent physical characteristics of the landscape

Dear list, I am modelling fish distributions using the glm-function followed by the step-function, and then want to cross-validate the model via the cv.glm-function from the {boot} package. I am working on fish distributions on coral reefs. The code I have works for one fish species. Since I have 227 fishes, I wrote a loop. Now the cv.glm-function comes up with an error message: "Error in

I have been working on the following code but keep getting an err message. My current thinking is that the problem is on the indexing but do not know how to fix it. Any help please? ungulate <- read.csv("Ungulate.csv",row.names=1) ungulate <- as.matrix(ungulate);colnames(ungulate)<-NULL;rownames(ungulate)<-NULL habitat <- read.csv("Ungulate_vegetation.csv")

Hello, I have some conflicting output from an aov summary and tukey contrasts with a mixed effects model I was hoping someone could clarify. I am comparing the abundance of a species across three willow stand types. Since I have 2 or 3 sites within a habitat I have included site as a random effect in the lme model. My confusion is that the F test given by aov(model) indicates there is no

Hi, there I first sent this e-mail a couple months ago, to no avail.Since I am not a member on your mailing list, so could you please cc: a response to me? I'll be sure to check the list today for replies. I am currently attempting to perform an ANOVA with both nested and normal factors. My problem is that R is treating my nested factors the exact same way as it would interaction terms.