search for: smoothscattercalcdensity

Displaying 4 results from an estimated 4 matches for "smoothscattercalcdensity".

2015 Feb 18
0
smoothScatter() and the KernSmooth package
...gc=yeastGC[sub]))) > > MDPlot(data,c(1,3)) Error in loadNamespace(name) : there is no package called ?KernSmooth? Calls: MDPlot ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous> Execution halted I looked at the code in smoothScatter and there is a call to grDevices:::.smoothScatterCalcDensity(), which in turn calls KernSmooth::bkde2D(). This gets fixed by adding KernSmooth as suggested package in EDASeq. I was able to reproduce this issue (and the fix) in a small R package: https://github.com/drisso/rmini/tree/smooth I have a few questions: isn't it unusual the way smoothScatter...
2010 Feb 06
1
Why does smoothScatter clip when xlim and ylim increased?
Hi: Is there a way to get smoothScatter to not clip when I increase the xlim and ylim parameters? Consider the following example: set.seed(17) x1<-rnorm(100) x2<-rnorm(100) smoothScatter(x1,x2) #Now if I increase xlim and ylim notice that the plot seems to be clipped at the former xlim, and ylim boundaries: smoothScatter(x1,x2, xlim=c(-5,5), ylim=c(-5,5)) Thanks. Jen sessionInfo() R
2009 Apr 22
1
reversing xlim, ylim in smoothScatter
....R in the lines : x <- x[ xlim[1] <= x[,1] & x[,1] <=xlim[2], ]   (line  number 25) and x <- x[ ylim[1] <= x[,2] & x[,2] <= ylim[2], ]  (line number 31) This results in a x being NA if ylim[1] > ylim[2] which results in an error on executing  map <- grDevices:::.smoothScatterCalcDensity(x, nbin, bandwidth) To counter this problem, I replaced the above two lines by : x <-x [min(xlim) <= x[,1] & x[,1] <= max(xlim), ] and x <- x[min(ylim) <= x[,2] & x[,2] <= max(ylim), ] and now smoothscatter reverses axes properly if xlim and/or ylim are provided with r...
2010 Apr 19
1
densCols: what are the computed densities and how to create a legend
Hi, I'm using the densCols function for a scatterplot and cannot figure out 1) how to extract the computed densities, and 2) how to create a legend based that represents the upper and lower ranges of the densities. For example: movers.den <- densCols(move$x, move$y) table(movers.den) #08306B #083775 #083B7C #083D7E #3989C1 #3F8FC4 28 22 101 25