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Dear R-users, Hereby a polar plot function for plotting angular data. I hope it will be usefull for some of you. I had a need to plot frequencies of wind-directions. The not-that-cheap SigmaPlot software did not allow me to change the orientation of the angular axis to clockwise orientation (what is used for meteorological observations). I even tried the latest version availible at the time

Hi R users, This query is regarding the use of the 'envelope' function in Spatstat. My data can be represented as a point process with CONTINUOUS marks: points <- ppp(x=x,y=y, marks=m, window= wind) However the marks are alignments (lines), and so have to be treated differently to normal scalar marks. Hence to create a mcf object with the appropriate test function for alignment

Dear R-users, As noted by Paul Murrell < p.murrell at auckland.ac.nz > there is errors in the code for polar plotting I send to R-help under the title "Polar plot, circular plot (angular data)" at Thu Oct 17 2002 - 12:18:20 CEST. Thanks! I have reorganized the code into a structure ('pp'). This allows plots to be modified to a greater extent by passing arguments by ...

...te bins for each factor combination for (i in 1:length(unique(subs))) { good <- subs==i assign(paste("nam",i,sep=""), erode.hexbin(hexbin(x[good],y[good],xbins=23,xbnds=rx,ybnds=ry))) } nam <- matrix(paste("nam",1:6,sep=""),ncol=3,byrow=TRUE) rlabels <-c("Females","Males") clabels <- c("Age <= 45","45 < Age <= 65","Age > 65") zoom <- hmatplot(nam,rlabels,clabels,border=list(hbox=c("black","white"), hdiff=rep("white",6))) I get...

I normally use the following code to create a figure displaying the mark correlation function for the point pattern process "A": M<-markcorr(A) plot(M) I have now started to use the following code to perform 1000 Monte Carlo simulations of Complete Spatial Randomness (CSR). It is a Monte Carlo test based on envelopes of the Mark correlation function obtained from simulated point

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

Hi, I am trying to test the random labeling hypothesis with my data. I have a point pattern of bird nests and each nest has a mark that it is either predated or not-predated. I want to test the null hypothesis that the K distribution of predation events is equal to the K distribution of all events (nests). This is the code I am using: Kdif <- function(X,...,i) { Kidot <-

...lasses, method = "svmRadial", preProcess = c("center", "scale"), metric = "cRand", tuneLength = 4) svmNew ------------------- here is an example on how to train the ksvm with spectral kernel ------------------- # Load the data data(reuters) y <- rlabels x <- reuters sk <- stringdot(type="spectrum", length=4, normalized=TRUE) svp <- ksvm(x,y,kernel=sk,scale=c(),cross=5) svp ----------------- Does anyone know how I can train the svm from above with using the caret package? best regards [[alternative HTML version deleted]]