search for: herbivores

I'm having the most curious error while using the sem package. For the model I'm working with, I keep getting the following error: Error in J[cbind(1:n, observed)] <- 1 : subscript out of bounds I''ve used debug=TRUE with sem, and there don't appear to be any problems with model - there are no latent variables in this model. The variables in the covariance matrix

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 = Distance to heterospecific source plant Distance should be nested within Subpopulation which in turn should be nested within...

...si(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 controls (C), ungrazed horse exclosures (H)with 3 wire strand fence to allow other herbivores but not horses and ungrazed full netting exclosures to exclude all herbivores (A) Site - 6 levels for 6 Sites which are considered fixed Time - 6 levels also for 6 different monitoring or time periods which is where the repeated measures comes in In SPSS there is the option for Bartlett's t...

...si(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 controls (C), ungrazed horse exclosures (H)with 3 wire strand fence to allow other herbivores but not horses and ungrazed full netting exclosures to exclude all herbivores (A) Site - 6 levels for 6 Sites which are considered fixed Time - 6 levels also for 6 different monitoring or time periods which is where the repeated measures comes in In SPSS there is the option for Bartlett's t...

...lose to this access spur. So I do three analyses when relevant, one looking at the three factors and then taking site out and replacing it with graze and then dam. It is a grazing exclusion experiment to test effects of feral horse grazing on a number of variables whereby C=controls so open to all herbivores, HE= Horses excluded but allowing other herbivores such as macropods, rabbits, and AHE= is a fully enclosed high wire fence excluding all herbivores. One treatment exclosure type was erected at each site but within these 30m2 exclosures there were two randomly located 5x6m plots so that is why the...

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):

...28f6446fo7770d24a6cb0d995@mail.gmail.com> Content-Type: text/plain; charset=ISO-8859-1 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 = Distance to heterospecific source plant Distance should be nested within Subpopulation which in turn should be nested within...

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