similar to: GLMMPQL spatial autocorrelation

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)

Hi all, I am analyzing data on habitat utilization of seals in the Southern Ocean. My data show spatial autocorrelation, which I'm interested in incorporating into my model. I am trying to model the presence of dives (versus simulated pseudo-absences) using a binomial generalized binomial model (glmmPQL), since I can incorporate the autocorrelation structure to the model using that package.

Hi, I have collected data on trees from 5 forest plots located within the same landscape. Data within the plots are spatially autocorrelated (calculated using Moran's I). I would like to do a ANCOVA type of analysis combining these five plots, but the assumption that there is no autocorrelation in the residuals is obviously violated. Does anyone have any ideas how to incorporate these spatial

Dear all, I am experiencing problems with glmmmPQL. I am trying to analyze binomial data with some spatial autocorrelation. Here is my code and some of the outputs > colnames(d.glmm) [1] "BV" "Longitude" "Latitude" "nb_pc_02" "nb_expr_02" [6] "pc_02" "nb_pc_07" "nb_expr_07"

Dear All Hopefully someone out there can point out what I am missing! I have a (large, several hundred) dataset of gardens in which over two years the presence/absence of a particular bird species is noted each week. I have good reason to believe there is a difference between the two years in the weekly proportion of gardens and would like to assess this, before going on to look in more detail at

Hello, I have a matrix with the distances among sites. And I have another matrix with the presence and absence of each species in each site. I would like to test the spatial autocorrelation among sites. I have tried to use the function gearymoran of the ade4 package, but error messages keep popping up. Do you know any function for me to test the spatial autocorrelation of my data? Thanks,

Hi R helpers, Is there a function (package?) in R available which tests "spatial autocorrelation" between points (e.g. vector layer of weather stations)? (e.g. Moran's I...) Via the archives we found out that there is a package 'spdep' which uses grid data for testing spatial autocorrelation. Thanks a lot, Jan

Howdy All, I am looking for some good tutorials (books, websites, whatever) for calculating/testing for Spatial Autocorrelation using R. Specifically, I am wanting to test for autocorrelation of a number of variables measured at a set of discrete locations. Up to this point I have been exploring the "spdep" package and I can get "moran.test" to work, but I am concerned that

Hello folks, I have some data where spatial autocorrelation seems to be a serious problem, and I'm unclear on how to deal with it in R. I've tried to do my homework - read through 'The R Book,' use the online help in R, search the internet, etc. - and I still have some unanswered questions. I'd greatly appreciate any help you could offer. The super-super short explanation is

Dear all, I am currently facing a problem related to the spatial autocorrelation of a sample of stations; these stations supply weekly data for a fixed time-window during the year (namely, 4-6 months per year). For this reason I'm trying to use the R package 'spdep' (specifically Moran's I) in order to get rid of it. Does anyone know how is it possible (if it is...) to

Dear all: Does anyone have any suggestions on how to make a spatially explicit landscape with spatial autocorrelation in R? In other words, a landscape where all cells have a spatial reference, and the environment values that are closer in space are more similar (positive spatial autocorrelation). Thank you, Laura

Dear all: I would like to create a landscape of environmental values that follow a uniform frequency distribution and also have spatial autocorrelation in the landscape. I was wondering if there is an algorithm and/or package out there that creates autocorrelation of values that are distributed according to a non-normal frequency distribution. Any suggestions are greatly appreciated. Thank you,

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

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

Dear Users: I have been trying to use the geary.test() function in *R*, but am having slight difficulty understanding how I am to apply it in my context. I have 2 matrices: 1) *n x p* matrix of *n* observations with *p* measurements each. It may be noted that this matrix has a spatial dimension to it, as the *n*observations are at different geographical locations on a map. 2) *n x n* spatial

Hi, I am new to this list. I have a question regarding including both spatial and temporal random factors in lmer. These two are not nested, and an example of model I try to fit is model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year), family=poisson, REML=FALSE), where richness = integer Y & Treatment = factor Canopy & Veg_cm = numerical, continous

I have spatial data on a sphere (the Earth) for which I would like to run an gls model assuming that the errors are autcorrelated, i.e. including a corSpatial correlation in the model specification. In this case the distance metric should be calculated on the sphere, therefore metric = "euclidean" in (for example) corSpher would be incorrect. I would be grateful for help on how to

Dear listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance

I'm a bit puzzled by how to write out additive random effects in glmmPQL. In my situation, I have a factorial design on two (categorical) random factors, A and B. At each combination, I have a binary response, y, and two binary fixed covariates, C and D. If everything were fixed, I would use glm(y ~ A + B + C + D, family = binomial) My first thought was to use glmmPQL(y ~ A + B, random

Dear R-help, I recently tried implementing glmmPQL in 2.3.1, and I discovered a few differences as compared to 2.2.1. I am fitting a regression with fixed and random effects with Gamma error structure. First, 2.3.1 gives different estimates than 2.2.1, and 2.3.1, takes more iterations to converge. Second, when I try using the anova function it says, "'anova' is not available