search for: rlabel

...ld be nice to have a polar plot function as a more permanent part of R! Thanks to Ross Ihaka at R-help (Mon May 28 2001) for some of the polar.plot code used. Best wishes, Karsten ######### "polar.plot" <- function (r, theta, theta.zero = 0, theta.clw = FALSE, method = 1, rlabel.axis = 0, dir = 8, rlimits = NULL, grid.circle.pos = NULL, grid.lwd = 1, grid.col = "black", points.pch = 20, points.cex = 1, lp.col = "black", lines.lwd = 1, lines.lty = 1, polygon.col = NA, polygon.bottom = TRUE, overlay = NULL, pi2.lab = TRUE, text.lab = NULL,...

...nction for alignment marks, I input 'func' (below) suggested by Stoyan & Penttinen (1989): func <- function(m1,m2) { sin(abs(m1-m2))^2} mcf <- markcorr(points, func, normalise = TRUE, method="density") So far, so good. However, usinf 'envelope' and 'rlabel' I would like to check if the pattern in the data is lost when randomly relabeling the mark for each point. If the test function, 'func' were the usual G(m1,m2)=m1*m2, then the following would work: E <- envelope(points, markcorr, nsim=20, simulate=expression(rlabel(points)))...

...axis. pp$text.lab<<- expression(0, pi/2, pi, 3*pi/2) # default text for angular axis labels pp$num.lab <<- NULL # (pretty) numeric angular axis labels in interval [0;num.lab[. If num.lab is a vector longer than 1 these are used as labels except the last value. (default = NULL). pp$rlabel.axis <<- 0 # angular orientation of radial axis (tick marks and labels) on the output plot. # # pp$radial.axis.labels: _method_ (plotting of radial axis labels): # NULL: no radial labels. # 1: labels at pretty radial distances (default). # 2: exclude label at radial distace 0. # 3: excl...

...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 ge...

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

...00 permutations would be a reasonable starting point (the ppp object has 27 points). so far, I've figured out how to: -create a marked ppp object: ms.ppp -calculate my test statistic: teststat <- mean(abs(markstat(ms.ppp, diff, N=2))) -randomly allocate marks to a point pattern: Y <- rlabel (ms.ppp, labels=ms.ppp$marks, permute=TRUE) I have looked at perm.test{exactRankTests} and perused the R help archive but haven't been able to find how to work the permutation test with a marked ppp object. I thank you in advance for any help, J-F Savard Doctoral Candidate University of Mar...

...ion events is equal to the K distribution of all events (nests). This is the code I am using: Kdif <- function(X,...,i) { Kidot <- Kdot(X,...,i=i) K <- Kest(X,...) dif <- eval.fv(Kidot-K) return(dif) } E <- envelope(ppp, Kdif, nsim=100, i="Predated", simulate=expression(rlabel(ppp))) plot(E, main="clustering of predation events") I got this code from the 'Analazing spatial patterns in R Workshop notes', and it works fine, but not sure I understand exactly what it means. Specifically I want to make sure that the Kidot is just looking at the distributio...

...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]]