similar to: map similarity spatial autocorrelation in R

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,

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 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, [note: 4th posting trial - apologize if the other ones would ever show up...] I have a (3-level) ordinal response data set which needs the integration of an spatial autocorrelation structure. What packages / functions are available to fit such a thing ? The heterogeneous, cluster-alike structuring of the autocorrelation seems to make a mixed effects model with random factors capturing

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

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

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

Dear all, I collected my data from the different agricultural fields every week over a period of a month. how can I test for spatial autocorrelation in R with data that are temporally pseudoreplicated? I used lme with correlation=corCompSymm(form=~Date) to model temporal pseudoreplication. Regards, VG

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

Hello all, This may be a simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas with unbalanced design in R. In my case, I have 11 vegetation transects (with 1000 10cmx10cm squares), where we estimated shrub cover. We have two different treatments: wildfire (4 transects) and prescribed burning (7 transects) and we want to

I have been reading about autocorrelation in linear models over the last couple of days, and I have to say the more I read, the more confused I get. Beyond confusion lies enlightenment, so I'm tempted to ask R-Help for guidance. Most authors are mainly worried about autocorrelation in the residuals, but some authors are also worried about autocorrelation within Y and within X vectors

Hi, I am looking at the effects of two explanatory variables on chlorophyll. The data are an annual time-series (so are autocorrelated) and the relationships are non-linear. I want to account for autocorrelation in my model. The model I am trying to use is this: Library(mgcv) gam1 <-gam(Chl~s(wintersecchi)+s(SST),family=gaussian, na.action=na.omit, correlation=corAR1(form =~

Dear users, I have two continuous variables which are two different measures taken each year from 1975 to 2005. I want to see if the two variables are correlated but need to take into account the fact that they are a time series. I have been following an example from 'The R Book' where you plot the ACF: par(mfrow=c(1,1) acf(cbind(x,y)) and this appeared to work fine, producing four

Hello everyone, I'm working on a current linear model Y = a0 + a1* X1 + ... + a7*X7 + residuals. And I know that this model presents both heteroskedasticity (tried Breusch-Pagan test and White test) and residuals autocorrelation (using Durbin Watson test). Ultimately, this model being meant to be used for predictions, I would like to be able to remove this heteroskedasticity and residuals

Hi, I am attempting to correct my models p-values due to temporal autocorrelations. It is not possible to model the correlation, so I have to make do with the p-value correction. I am feeling a bit thick here....I cannot get the autocorrelation values. What is the argument? My aim is to multiply the dependent variable autocorrelation with the independent variable autocorrelation and then

Dear All, Would like to ask the inconsistency in the autocorrelation from R with SPSS/Minitab. I have tried a dataset x with 20 data (1-20) and ask R to give the autocorrelation of different lags using the command < acf(x, lag.max=100, type = "correlation"), However while SPSS and Minitab give the same answers (0.85 for lag1), R gives 0.3688 which is much smaller. Obviously, the