similar to: glmmPQL and spatial autocorrelation

Hi all, I'm running into some computer issues when trying to run a binomial model for spatially correlated data using glmmPQL and was wondering if anyone could help me out. My whole dataset consists of about 300,000 points for which I have a suite of environmental variables (I'm trying to come up with a habitat model for a species of seal, using real (presence) and simulated dives

Dear all, I am experiencing problems using the glmmPQL function in the MASS package (Venables & Ripley 2002) to model binomial data with spatial autocorrelation. My question - is the presence of birds affected by various hydrological parameters? Presence/absence data were collected from 83 sites and coupled against hydrological data from the same site. The bird survey sampling effort

Hi, I have a spatial data set with many observations (~50,000) and would like to keep as much data as possible. There is spatial dependence, so I am attempting a mixed model in R with a spherical variogram defining the correlation as a function of distance between points. I have tried nlme, lme, glmmML, and glmmPQL. In all case the matrix needed (seems to be (N^2)/2 - N) is too large for my

Hi. I'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)

I'm using R 1.6.2 on a Windows 2000 machine. I've plotted the results of an MDS run labeled by a numerical ID, and color coded by a group code: plot(cv.mds.spr$points, type="n", main="Non-Metric Multidimensional Scaling of SprRun CV Watersheds") text(cv.mds.spr$points, labels = as.character(cv.wshed.id.spr), col = codes(cv.wshed.grp), cex=.75) Question is, how do I

I am trying to do a Nonparametric Tukey-type multiple comparison post-hoc test to determine which groups are significantly different. I have read the dialogue on this topic from the R-help, and am still not clear why no statistical packages include this test as an option? Is it not an appropriate test to conduct on non-normally distributed data? Is the only option to calculate it by hand

I don't use R much, and I have been unable to figure out how to get the subset of my data frame that I would like. For example, if this were my data frame: > dfr <- data.frame(x=rep(letters[1:3], 4), y=(1:12), z=(LETTERS[1:12])) > dfr x y z 1 a 1 A 2 b 2 B 3 c 3 C 4 a 4 D 5 b 5 E 6 c 6 F 7 a 7 G 8 b 8 H 9 c 9 I 10 a 10 J 11 b 11 K 12 c 12 L I would like to

Easy question that I can't find an answer for. I'm trying to subset a data frame and want to exclude the positive values, i.e. I want the NA values. My data: > summary(temp$tuna) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 1 2 3 3 4 5 1211 Querying for subset(temp, tuna %in% "NA", select.... subset(temp, tuna == NA,

The following should work: > dfr.samp <- dfr[tapply(1:nrow(dfr), dfr$x, sample, 1),] > dfr.samp x y z 10 a 10 J 2 b 2 B 9 c 9 I Andy From: Kelly Hildner > > I don't use R much, and I have been unable to figure out how > to get the > subset of my data frame that I would like. > > For example, if this were my data frame: > > > dfr <-

Hello. I'm pretty much new to R and I'm trying to produce some figures. It seems to me, that R has some asynchronous way of plotting figures. When I run this code: #constructs the semivariogram of SC1929 vgm1 <- variogram(SC1929~1,~U+V,puerto.map$att.data) # trying to make new plot dev.set(which=dev.next()) plot(vgm1) title(main="Semivariogram",font.main=4)

I have a dataframe that I'm trying to reshape, and need advice. My data: > klam.merge[200:225,] stream lulc x sumlength pct.lgth 200 1223030419685 92 0.25000000 9.89 2.52780586 201 1223030419686 23 0.00274154 4.73 0.05796068 202 1223030419686 41 0.75009917 4.73 15.85833341 203 1223030419686 42 2.65000000 4.73 56.02536998 204

Hello All, I am looking for an R library/function that allows the specification of a custom link function in a linear mixed model. I've been using glm() in library MASS to fit fixed-effect models with a custom link but my study design demands mixed models. Any suggestions on the best R library/function to achieve this would be greatly appreciated. I have tried, to no avail, to

Hello, I'm using the gstat package within R for an automated procedure that uses ordinary kriging. I can see that there is a logical ("singular") atrtibute of some adjusted model semivariograms: .- attr(*, "singular")= logi TRUE I cannot find documentation about the exact meaning and the implications of this attribute, and I dont know anything about the inner calculations

Does the polymars library exist anymore? I see reference to it at http://rweb.stat.umn.edu/R/doc/html/packages.html, but not on CRAN (looked in contrib and contrib/Old). -- Rob Schick Ecologist NOAA Fisheries Santa Cruz Lab 110 Shaffer Road Santa Cruz, CA 95060 Phone: 831.420.3960 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I'm using R 1.6.2 on Windows 2000. I have two similar sets of files in two different directories. One contains species presence data and environmental measurements; the other contains species absence data and environmental measurements for the same variables as the presence data. The absence datasets contain many more observations than the presence datasets, and I'd like to draw a

Hello - I am attempting to add interaction terms to a model using the gamm function in the mgcv package. I stripped it down to only two terms to simplify it. I have no trouble with individual terms, continuous or factors, nor with interacting factor terms; I cannot, however, figure out how to add interactions between continuous*factor terms. I applied a smoother to my continuous term because it

Hello All - Any recommendations or suggestions for neat ways to visualize data taken from a 3-axis accelerometer? My study species is aquatic, so I would be interested in movement in the 3 dimensions in addition to being able to incorporate the time series as well. Is there a package in R that might be useful for this? Thank you in advance, Jen ~~~~~~~~~~~~~~~~~~~~~ Jennifer Maresh, PhD

Hi- I am interested in running the BCP model in R and I am also new to R. My main issue is how to ask a specific questions within the script for this model, i.e., how one or multiple dependent factors (blue) are interact with the dependent factor (yellow), and nest the question do categorical factors (green) changes those interactions. I have attached some fake data in the link below with

Hi, I've got a new dataset which I don't know how to analyze with R. My knowledge about R is limited for this kind of problem. I've tried to find a solution with some spatio temporel packages and lme/lmer functions, but didn't find any similar example. I've got 10 locations on a coast on which the number of walrus was noted. I've got a distance measure between each

This isn't an R issue per se, but given the recent issues with Mozilla and help.search(), I figured I'd float it. I use mozilla on a WIN2000 box. I was using 1.0 and the help.search() wasn't working as has been discussed over the past few days. Yesterday I upgraded to 1.1, and the javascript help still does not work. (I checked and it is enabled.) Another problem I noticed this morning