similar to: nested repeated measures in R

Hi, I have three years of species presence/absence data for a set of ~100 ponds, with a list of associated habitat characteristics (park, vegetation, hydroperiod, etc.). The datasets differ slightly by year because some ponds were dry in some years and not in others. My goal is to look at habitat associations for a couple focal species. For each year, I have a data table that looks like

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Hello, We are attempting to use nlme to fit a linear mixed model to explain bird abundance as a function of habitat: lme(abundance~habitat-1,data=data,method="ML",random=~1|sampleunit) The data consist of repeated counts of birds in sample units across multiple years, and we have two questions: 1) Is it necessary (and, if so, how) to specify the repeated measure (years)? As written,

Hello, I am trying to fit a repeated measures linear mixed model (using lme) but I don't want to include any random effects. I'm having trouble (even after consulting Pinheiro & Bates 2000) figuring out how to specify the repeated measure without including it in the specification of a random effect. My data consist of repeated "counts" in "plots" that I wish

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 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 R community, I have some questions regarding the analysis of a zero-inflated count dataset and repeated measures design. The dataset is arranged as follows : Unit of analysis: point - these are points were bird were counted during a certain amount of time. In total we have about 175 points. Each point is located within a certain habitat fragment (here: "site"= A-B-C-D-..., in

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.

Dear listers, I am trying to assess variance components for a nested, mixed-effects model. I think I got an answer that make sense from R, but I have a warning message and I wanted to check that what I am looking at is actually what I need: my data are organized as transects within stations, stations within habitats, habitats within lagoons. lagoons: random, habitats: fixed the question is:

All, I'm trying to analyze the results of a reciprocal transplant experiment using lme(). While I get the error-term right in aov(), in lme() it appears impossible to get as expected. I would be greatful for any help. My experiment aimed to identify whether two fixed factors (habitat type and soil type) affect the development of plants. I took soil from six random sites each of two types

Dear all, I studied in tank prey fish behavior. Using the design described below (and R code), I want to test the effects of both habitat and predator (and interaction) on prey fish's vertical distribution, which was recorded (with repeated measures) as a categorical variable. I found that package mlogit might fit to my need but I don't know how to specify my complex design in the

> library(SpatioTemporal) > library(plotrix) > library(maps) > palay.cov<-read.csv("C:/Users/BEDANA-PC/Desktop/STThesisWD/Thesis Data/palay.covar.csv") > palay.o<-read.table("C:/Users/BEDANA-PC/Desktop/STThesisWD/Thesis Data/palay.obs.txt") > palay.obs<-as.matrix(palay.o) > palay.stc<-read.table("C:/Users/BEDANA-PC/Desktop/STThesisWD/Thesis

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

Hi, I'm trying to compute  the spatiotemporal correlation matrix by using Delta Kronecker products of spatial and temporal correlation matrix  in R, but didn't find any delta Kronecker's operator in R. The operators in matrix such as multiplication, addition, eigen values/vector and etc is easily to find and used. Could someone help me, please? Cheers. Firdaus

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")

Hi, I'm working on the codes below however every time I run them when they get to OpenBUGS I keep getting the error message: array index is greater than array upper bound for hab. Any help would be greatly appreciated, Suzie Codes: ungulate <- read.csv(file.choose ()) #ungulate ungulate <- as.matrix(ungulate);colnames(ungulate)<-NULL;rownames(ungulate)<-NULL

hi all, I often have a data frame like this example data.frame(sq=c(1,1,1,2,2,3,3,3,3),area=c(1,2,3,1,2,3,1,2,3),habitat=c("garden","garden","pond","field","garden","river","garden","field","field")) for each "sq" I have multiple "habitat"s each with an associated "area". I

Hi all, I have some concerns regarding the rasterize option in R and I would like to know if the fun=max in rasterize in R provides similar results to the one achieved by using "Polygon to Raster using maximum-combined-area" in ArcGIS? I'm trying to rasterize a habitat layer to a raster of 10m spatial resolution using the function 'max' (e.g. r <- rasterize(ht, r,

Hi all, This is partly a statistical question as well as a question about R, but I am stumped! I have count data from various sites across years. (Not all of the sites in the study appear in all years). Each site has its own habitat score "habitat" that remains constant across all years. I want to know if counts declined faster on sites with high "habitat" scores. I can

Hello from a presently sunny Helsinki! I've bee trying to repeat an analysis I did about 18 months ago (the reviewers of the paper want something adding to it). I'm using R1.5.0, but I couldn't see anything in the list of changes between this and 1.5.1 to suggest it would act any differently. The data is observational, on the changes in the population status of carabid beetles, and