similar to: a question about spatial autocorrelation in R

Hi I am working with the implementation of Samba(3.0.7) against AD. I compliled Samba after compiling LDAP, kerberos.I can execute the following commands successfully. wbinfo -u, -g -t netads info, testjoin getent passwd group But i cant use chown to use the owner as AD user, even after shutting down the nscd daemon. I am giving the my smb.conf file [global] workgroup = SE

Dear R Fundation, I am a post-doc researcher at the University of Pisa, Italy. I apologize for my english and I have to tell you in advance that I am a very beginner with R. I used R for fitting dose-response curves (drc package) and for an ordinary ANOVA (one, two or three factors), including the post-hoc mean comparison (I used the LSD test...). Now I have to process some simple data on

Hello, I would like to place region names (or abbreviations if space is too limited) on a map. I have tried the following, copying and adapting various examples I have seen but without success. The first example is self-contained, but the second one requires the .shp file, which can retrieved at http://www.gadm.org/country. ----- Example 1 ----- library(sp) con <-

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

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

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

Greetings! (and apologies upfront for any duplication as a result of my cross-posting) It''s been five weeks now since I began working with RoR. My experience has been incredibly positive. So much so that I''m emboldened to undertake a project I''d previously concluded I no longer had the technical chops to even _start_. I''ve included a brief description of

Hi, I have two global raster maps, each of the same variable but from different sources. The values range from 0 to 5 in whole numbers. Is there a statistical test in R that can quantify the similarity of the spatial patterns (i.e., highs and lows)? Thanks, -- View this message in context: http://r.789695.n4.nabble.com/map-similarity-spatial-autocorrelation-in-R-tp4647877.html Sent from the

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

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.

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 trying to calculate the autocorrelation matrix for an input matrix with the size n*m where n=7 (the dimensionality of my input feature vectors) and m being the time. Thus one could think of the input data as a 7-dimensional time-series. Does anyone know of any way to calculate the autocorrelation matrix for such an input? I tried various functions, but none give me a matrix that returns

Hi,   I'm actually I’m performing a TSLS linear multiple regression on annually data which go from 1971 to 1997. After performing the TSLS regression, I tried to extract the R squared value using “output$r.squared” function and to perform autocorrelation (Durbin Watson and Breush Godfrey) and heterokedasticity tests (Breush-pagan and Goldfeld Quandt)  but I have errors messages. More

Hey all! I am working on my master thesis and I am desperate with my model. It looks as following: Y(t) = ?1*X1(t) + ?2*X2(t) + ?*(?1*((1+c)/(?+c))+?2)*IE(t) - ?2*?*((1+c)/(?+c))*(?+g)* IE(t-1) note: c and g is a constant value The problem I encounter is that between IE(t) and IE(t-1) there is strong linear correlation (autocorrelation). How can I solve this problem? Of utterly importance is

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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

Dear users I am fitting a Generalized Additive Mixed Models (gamm) model to establish possible relationship between explanatory variables (water temperature, dissolved oxygen and chlorophyll) and zooplankton data collected in the inner and outer estuarine waters. I am using monthly time-series which are auto-correlated. In the case of the inner waters, I have applied satisfactoryly (by

Hi R users, where can I find the equations used by acf function to calculate autocorrelation? I think I misunderstand acf. Doesn't acf use following equation to calculate autocorrelation? [image: R(\tau) = \frac{\operatorname{E}[(X_t - \mu)(X_{t+\tau} - \mu)]}{\sigma^2}\, ,] If it does, then the autocorrelation of a sine function should give a cosine; however, the following code gives a

Hi all, Does anyone know how to report heteroskedasticity and autocorrelation-consistent standard errors when using the "spm" command in SemiPar package? Suppose the original command is sp1<-spm(y~x1+x2+f(x3), random=~1,group=id) Any suggestion would be greatly appreciated. Thanks, Susan [[alternative HTML version deleted]]