similar to: error running mvabund package

Dear R users, I am trying to analyze multivariate abundance data using the mvabund package, but errors occur for several functions. I created a mvabund object named faunadat consisting of 8 dependent variables each containing 64 positive integer values (bact, fung, plant, omn, pred, orib, meso, spring). There are 4 factors: harv, rep, depth and lit. I then created a formula: >

Hi everyone, I am trying to construct a glm and am running into a couple of questions. The data set I am using consists of 6 categories for the response and 6 independent predictors representing nutrient concentrations at sample point locations. Ultimately I'd like to use the probabilities for each response category in a simulation model such that these probabilities are used to define a

Full_Name: Charles J. Geyer Version: 1.8.0 OS: i686-pc-linux-gnu (Suse 8.2) Submission from: (NULL) (134.84.86.22) Two bugs (perhaps related, perhaps independent) revealed by the same Poisson regression with offset mydata <- read.table(url("http://www.stat.umn.edu/geyer/5931/mle/seeds.txt")) out.fubar <- glm(seedlings ~ burn01 + vegtype * burn02 + offset(log(totalseeds)),

I've been working on a multinomial logit model, trying to predict vegetation types as a function of total phosphorus. Previous responses to my postings have pointed me to the mlogit package. I'm now trying to work examples and my data using this package. data("Fishing", package = "mlogit") Fish <- mlogit.data(Fishing, varying = c(4:11), shape = "wide",

Hello, I would like to produce a series of graphs comparing the probability distributions for 8 factors against a continuous metric. The kind of graph I'm hoping to produce would look like the density comparison graphs (library sm) using the function sm.density.compare. However, instead of calculating the density distributions for comparisons, I'd like this comparison to be based on

I am sure this question has come up, but searching hasn't given me any results. So I need to enter this line: mx1 <- randomForest(X1 ~ elevation + slope + vegtype, data = moths.train) But the problem is that X1 is currently hard coded. I would instead like to be able to put in the value of X1 through a list. For example: list <- list("X1", "X2", "X3")

Dear friends, when i use glm() to fit my data, i use glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, *family = Gamma(link = inverse),* data =a,)) It shows: error in eval(expr, envir, enclos) : *gamma distribution don't allow negative value*. But i use result<-glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, family = poisson, data =a) #this

Dear all, I would like to use the values in vegaggr.BLMCMR02$colony str(vegaggr.BLMCMR02) `data.frame': 1678 obs. of 3 variables: $ vegtype : Factor w/ 27 levels "2010","2020",..: 3 4 5 19 4 5 19 5 $ colony : Factor w/ 406 levels "0","1","10","100",..: 1 1 1 1 2 2 2 $ Totvegproparea: num 0.00055 0.03956 0.95705

We have seen problems with a number of packages which use R/Rscript to run R code in configure or makefiles. (a) You must give a full path: there need be no version of R in the path, and if there is it might not be the version/build of R under which package installation is being done. So the general form is to use ${R_HOME}/bin/R to select the right version. And since ${R_HOME} might

We now have the integrated 32/64-bit installer for R-devel available from http://cran.r-project.org/bin/windows/base/rdevel.html , and binary builds of almost all the CRAN packages from CRAN or CRAN extras (and BioC 2.7 has a fairly complete 32/64-bit repository). There is also a win-builder service available for R-devel. These are built with different toolchains from R 2.11.x, using gcc

I'm in the process of converting some S3 methods to S4 methods. I have this function : setGeneric("enrichmentCalc", function(rs, organism, seqLen, ...){standardGeneric("enrichmentCalc")}) setMethod("enrichmentCalc", c("GenomeDataList", "BSgenome"), function(rs, organism, seqLen, ...) { ... ... ... })

Dear all, I have run the confint() function according to below and I get the following error message: > confint(stepAIC.glm.spe.var.konn2.abund, level=0.95) Waiting for profiling to be done... Error: missing value where logical needed In addition: Warning message: NaNs produced in: sqrt((fm$deviance - OriginalDeviance)/DispersionParameter) or > confint(stepAIC.glm.spe.var.konn2.abund,

Buenas tardes, tengo 2 dfs: Dieta de (108x11) y Abund de (591x108). Necesito multiplicar cada columna de la 1ª (108 elementos) por cada fila de la 2ª (108 elementos) y crear una nueva df con las sumas de esas multiplicaciones. He hecho esto, pero no sale y creo que está lejos de estar bien: Res <- matrix(nrow=nrow(Abund),ncol=ncol(Dieta)) Res <- as.data.frame(Res) for(i in

Dear R users, I am having problems trying to fit quasipoisson and negative binomials glm. My data set contains abundance (counts) of a species under different management regimens. First, I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter

Hello all, I'm a beginner in R working on a script that will produce a set of models (linear, polynomial and logistic) for each location in a dataset. However, the self-starting logistic model often fails - if this happens I would like to just skip to the next iteration of the loop using tryCatch. I've looked at a few examples and read the help file, but didn't understand tryCatch

Hi all, When upgrading to 2.11.1 recently I noticed different results being produced by my code. After much digging I have finally narrowed it to a call to nlm(). This can be replicated by: FixedRemovals<-1836180125888 AbStageInitial<-2223033830403 Rates<- 0.3102445 nlm(function(rootM,Abund,Loss,OtherM) {(Loss-(rootM/(rootM+OtherM)* (1-exp(-(rootM+OtherM)))*

Hello everybody, In am trying to analyse a BACI experiment and I really want to do it with R (which I find really exciting). So, before moving on I though it would be a good idea to repeat some known experiments which are quite similar to my own. I tried to reproduce 2 published examples but without much success. The first one in particular is a published dataset analysed with SAS by

It is a trivial problem, but in the book I couln`t figure out how to put different colours at different regression lines plot(bif,abund,type="n", xlab= "number_bifurcations", ylab="abundances") sbif<-split(bif,stage) sabund<-split(abund,stage) points(sabund[[2]],sbif[[2]],pch=16, col="red") for(i in 1:2) abline(lm(sabund[[i]]~sbif[[i]])) Thanks in

Dear R- Community, to learn i reanalysed some data provided and analysed by Zuur et. al. in their book "Mixed effect models and Extensions in Ecology with R". When i run the last command i get a warning message i dont understand. Loyn<- read.table(file = "loyn.txt",header = TRUE) Loyn$L.AREA<- log10(Loyn$AREA) fGRAZE <-factor(Loyn$GRAZE) M0<- lm(ABUND~ L.AREA

Hi, I try to make an analyses to discover what is the time that an area begin to have spacial autocorrelation. And after, what is the number of individuals responsible for this autocorrelation. The main idea is to discover if exist a contamination of a quadrat from others quadrats and how is the population needed to make this contamination. This is very common to use automata to simulate