search for: herbivorous

...d this with all sorts of subsets of the model, making sure to keep all of the proper error terms. --- fish.invert.data<-data.frame(Prev.Year.Predatory.Invert.Abundance, Prev.Year.Predatory.Fish.Abundance, Predatory.Invert.Abundance, Predatory.Fish.Abundance, Prev.Year.Herbivorous.Invert.Abundance, Prev.Year.Herbivorous.Fish.Abundance, Herbivorous.Invert.Abundance, Herbivorous.Fish.Abundance, Kelp.Abundance, Prev.Year.Kelp.Abundance, Mean.Temp.C, Prev.Year.Herbivore.Abundance) #na.rm=TRUE to deal with a few rows of missing data fi...

Greetings. I'm a Master's student working on an analysis of herbivore damage on plants. I have a tried running a glm with one categorical predictor (aphid abundance) and a binomial response (presence/absence of herbivore damage). My predictor has four categories: high, medium, low, and none. I used the "ordered" function to sort my categories for a glm. ah <-

Dear all, I'm quite sure that this is a stupid question, but I'll ask anyway. I want to perform an ANCOVA with two continuous factors and three categorical factors. Plant population growth rate (GR) = dependent variable Seed reduction due to herbivory (SR) = continuous explanatory variable Herbivore species (HS, 2 levels) = categorical explanatory variable Population (Pop, 24 levels) =

Hello I ran the code below but it said: no object "'panel.xyplot.intermediate.hh'" Please kindly advise how to modify the code. thank you. (It works with panel.bwplot.intermediate.hh) Elaine code library(HH) # data input dataN <-read.csv("H:/R_data/Mig_bird_586.csv",header=T, row.names=1) dim(dataN) dataN[1,] str(dataN) diet.code <-

Hello I want to draw a xyplot. Its dots will have three colors: red for meat, green for vegetable, and blue for both. I used the code below but could not make the dot in the same group show the same color. Please kindly advise how to modify it. Thank you. code library (lattice) diet.code <- c("Herbivore", "Omnivore", "Carnivore") Diet.colors <-

Dear members of the R help list, I want to do a hierarchical glm with binomial family but am unsure about how to write the syntax which involves nesting. I want to test whether the risk of being attacked by Herbivores for Meadowsweet plants is significantly dependent on the Distance to heterospecific source plants. Dependent variable = Herbivory (yes/no) Explanatory continuous variable =

Hello and thanks in advance I am running a glm in R the code is as follows with residual diagnostic code below model4<-glm(Biomass~(Treatment+Time+Site)^2, data=bobB, family=quasi(link="log", variance="mu")) par(mfrow=c(2,2)) plot(model2) to test the effect of grazing exclusion of feral horses for a Phd with following factors: Treatment - 3 levels which are grazed

Sorry, I just posted the email below but realised I did not give a name or details, would it be possible to adjust before posting and send what is below, sorry again, first time user... From: Joanne Lenehan [mailto:jlenehan@une.edu.au] Sent: Tuesday, 1 December 2009 3:51 PM To: 'r-help@r-project.org' Subject: GLM Repeated measures test of assumptions: e.g. test for sphericity e.g.

Good afternoon My name is Joanne Lenehan, I am a post grad at UNE using R version 2.9.0 I came across the Tweedie package in old R help posts and was interested in giving it a go for some data. The data is below and also attached as BaregroundLitterLogs Site Treatment Graze Dam Plot Time Bare Litter Logs 1 C remote yes 1 A 0 2 0 1 C remote yes 2 A 0 15 0 1 HE

Dear All! It's now weeks that I'm going crazy with R, and as I'm a new user I now ask for help (also because I still have only a few days to finish..)... So shortly I describe you my Experiment in which I was looking for the decomposition of herbivore dung under different treatments: I made a box design experiment which is structured in the following way: I collected dung from 2

A few general comments about stepwiseAIC and a suggestion of how to select models a) Apart form the problem, that stepwise selection is not a garanty to get the best model, you need to have a lot of data to avoid overfitting if your model includes 7 parameter plus interactions (> 10 observations per parameter is what you are ideally looking for). b) Have a look at Anderson and Burnham's

CRAN (and crantastic) updates this week New packages ------------ * disclapmix (0.1) Maintainer: Mikkel Meyer Andersen Author(s): Mikkel Meyer Andersen and Poul Svante Eriksen License: GPL-2 http://crantastic.org/packages/disclapmix disclapmix makes inference in a mixture of Discrete Laplace distributions using the EM algorithm. * EstSimPDMP (1.1) Maintainer: Unknown Author(s):

Hi r-users, I want to use the sn package but I got the following message: > install.packages(repos=NULL,pkgs="c:\\Tinn-R\\sn_0.4-12.zip") Warning: package 'sn' is in use and will not be installed updating HTML package descriptions I did tried to do it a few times but it gives the same message.  ________________________________ From:

To Whom It May Concern: I have an R package and want to put this package be part of R-project and available to anyone who is interested in. The R package is created for my paper, titled "Acceptance Sampling Plans from Truncated Life Tests Based on the Birnbaum-Saunders Distribution for Percentiles". The paper has been accepted by Communications in Statistics: Simulation and