similar to: using markstat

Hello! I am writing you because I could not plot the confidence envelopes for functions Jest, Jcross, Jdot, Jmulti, and L, using the Spatstat package. I have already understood how to do that for Kest or Jest, that is: JEnv <- plot(envelope(PPPData, Jest)) Where PPPData is my ppp object. However, for Jcross I must specify the two marks I want to analyse. That is, usually I would get the

Dear all, I have one Model (M3) fitted using the lme package, and I have checked the correlation structure of within-group errors using plot(ACF (M3,maxLag=10),alpha=0.05) But now I am not sure how to interpret this plot for the empirical autocorrelation function. The problem is that I am used to see/interpret diagrams in which all the autocorrelation Lags, except lag-1, are inside the

Dear all, I am using the spatstat package, but my field data was a mirror image of the data plotted by plot.ppp(X). Therefore I changed it using -x and -y coordinates. However, I guess (I saw "debug") that I get some error messages because of that. So, could anyone please tell me if I really can not use negative values for x and y coordinates in a ppp.object (and relates analysis)?

Hello! Could anyone please tell me how should I include R in a text section for References? Regards, Sara Mouro [[alternative HTML version deleted]]

Dear R users, I am trying to perform a hypothesis test on a marked point pattern. I would like to calculate the mean of the absolute value of the difference of marks between nearest neigbours, randomize the marks among points, then calculate this mean again. Ideally, I would test whether random mean values smaller than the observed mean value occur less than 5% of the time. I suppose 1000

Dear all, How can I explian and solve the error message: "margins too large" which appears when I do something like: KK <- alltypes(SpatData, "K") plot.fasp(KK) Hope someone can please help me on this. Regards, Sara Mouro [[alternative HTML version deleted]]

Dear R users, I have this 2 models that fit to my data: M3varI <- update (M3, weights=varIdent(form= ~ 1|SITE)) M3AR1<-update(M3,correlation=corAR1()) The first one, updates my M3 so that I can account for the variance structure of random erros. The second one, updates my M3 so that I can account for the correlation structure of random errors. How can I put them toghether in one

Dear All, I have to enter many lines of data in the same object. I usually use copy-paste to transfer data from an Word file to R. But, for large lines of data, R gets "confused" and gives an error message, i.e. it breaks one line somewhere, and lines get no meaning at all. Some times I solve that problem adding "enters" and making each line shorter, before I do

Dear all, I am using the spatstat package, in particular the Jest, Jdot and Jcross functions. When I plot the results using J <- Jest (SpatData) plot.fv (J, main="Recruits") there are 4 different lines (different colours and shapes): black _____ green -------- red _ _ _ _ blue _._._ Could someone please tell me which line is which?? Regards, Sara Mouro [[alternative HTML

Dear all, I am using the nlme package to find the best model using non-linear mixed effects model. After the all the other things needed, I did > M1<- nlme(NShrubs~A*exp(-B*CED),data=DataSg,fixed=list (A~SITE,B~1), random=B~1,start=list(fixed=c (1.66,1.66,1.66,1.66,1.66,1.66,1.66,1.66,0.0038)),control=lmc) and it was ok. However, the data showed to need random parameters at both

Dear all I can not understand how to install the package lpsolve_1.1.9.zip I have read the FAQ and the help pages carefully, but it still not clear for me. I have tried the following (and obtained the respective error messages): >install.packages("c:/ProgramFiles/R/rw2011/library/lpSolve_1.1.9",destdir=" c:/ProgramFiles/R/rw2011/library/lpSolve") Mensagem de aviso:

Dear all, Could you pleas advise me on the following? I need to use general(ized) linear models (binomial distribution + logit link function) , to describe the preferred environment of each species (each sample is an individual in which I have measured several variables and also recorded the species it belongs to) However, must account for the spatial autrefoocorrelation between

Dear all, I have read the on-line explanations I have found about Building R from sources, and istalling Packages from sources... Also, I have installed gcc4.0 and the gfrotran-4.2.1.dmg. However, I still do not know how to use them to compile one Source (in particular packfor_0.0-7.tar.gz) so that I can use it in R (for MAC OS X). Could someone please explain it to me in an easiest way

Hi there, is there any R-function for k-nearest neighbour agglomerative hierarchical clustering? By this I mean standard agglomerative hierarchical clustering as in hclust or agnes, but with the k-nearest neighbour distance between clusters used on the higher levels where there are at least k>1 distances between two clusters (single linkage is 1-nearest neighbour clustering)? Best regards,

Dear all, I am a beginner with lpSolve package (and not an expert in the others). I can not understand why I am doing wrong, and I would be very grateful if anyone could please help me on this. I am trying to optimize ("min") the sum of columns/variables, constrained to >=1. Each column/variables has its weight - given by values at f.obj. The matrix for numeric constraint

[[this bounced first, because it has 'help' in the Subject line ... -- Martin Maechler ]] I am a complete novice R programmer. (Though I know C quite well) I am trying to write some R code to do the following simulation. There is a 2-frame "movie" of noise and signal dots. the noise dots have random positions in each frame. The signal dots are placed randomly in frame 1,

I am extracting climate data for coastal areas from 5km grid data for specific xy coordinates that relates to individual study sites (SQ). Code so far: setwd("/Users/roblewis/Documents/PhD/Climate Data") fnamestemp=read.table("Path_Directory_Maxt") fnames=paste(fnamestemp[,1],fnamestemp[,2]) nfiles = length(fnames) xy=as.matrix(read.table("xy_.txt"))

I've taken a look at this. What the R code does is to recalculate the nearest neighbours & distances after updating the distances, for all clusters other than the new one which it attempted to do on the fly. The problem is that merging two clusters can make distances to the cluster go up and so what was a nearest neighbour may stop being so, as well as the reverse. So I am not convinced

Hello all, I am brand new to R and doing an exercise for a class. I need to find the nearest neighbour for points in the following matrix: > DistanceMatrix x1 x2 x3 x4 x5 [1,] 0.000000 2.828427 1.581139 2.236068 2.000000 [2,] 2.828427 0.000000 1.581139 4.123106 2.000000 [3,] 1.581139 1.581139 0.000000 2.549510 2.121320 [4,] 2.236068 4.123106 2.549510

Dear R users, I used nndist() to gain the distance of 2 nearest neighbours of the points in my dataset. Is there a way of getting the ID numbers of these nearest neighbours (along with their distances). The command I used: nn2 <- nndist(X2, k=1:2). This is the output of the nearest neighbour distances I got (the IDs of the given points are there but their IDs aren't). Any help or