similar to: smoothScatter on map

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

Dear all, I am using the smoothScatter function in base R for a plot - Lab.palette.both <- colorRampPalette(c("darkblue","lightblue","red","yellow"), space = "Lab") smoothScatter(X24fresh.sorted[,c(13,10)], colramp = Lab.palette.both) I understand that my yellow colored points represent highest density, red lower, lightblue even lower etc.

Dear R-devel, my Bioconductor EDASeq package has a function MDPlot that uses the smoothScatter() function from the graphics package. When I test this package on travis-ci.org (R CMD check) I get the following error (which I don't get on my machine nor on the Bioconductor build system). * checking examples ... ERROR Running examples in ?EDASeq-Ex.R? failed The error most likely occurred in:

On Tue, 18-Dec-2007 at 11:21AM -0500, James W. MacDonald wrote: |> Duncan Murdoch wrote: |> > Yes, I agree. (As an aside, there's actually a capital S in |> > smoothScatter(), and it's a bit of a pain to install, because |> > geneplotter depends on something that depends on DBI, which is not so |> > easily available these days.) |> |> Somehow I always

Hello, I'm having some trouble getting a good result for a smoothScatter plot. I have some data that I want to log-plot, but when I use smoothScatter the result is not correct. The problem seems to be that with the log="x" argument smoothScatter calculates the bins linearly, so the plot will be skewed towards the right. See for example:

Hi, I have been trying to plot density plots using the example on: http://addictedtor.free.fr/graphiques/graphcode.php?graph=139 I used to use this function, but I cannot get any old code or even the example to work. library("geneplotter") require("RColorBrewer") x1 <- matrix(rnorm(1e4), ncol=2) x2 <- matrix(rnorm(1e4, mean=3, sd=1.5), ncol=2) x <-

Hello, I have found that in smoothScatter it is not possible to reverse the axes plotted (R version 2.9.0) .   It appears that this arises from the hard coding of xlim and 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

Hello, I am attempting to use smoothScatter to plot a heatmap of locations of events in an x-y axis. When I plot the heatmap without passing xlim and ylim parameters, it fills the plot area but the perspective is a bit skewed. I would like to standardize these plots to a uniform window size that does not depend on the range of values in the dataframe. However, when I resize the plot using xlim or

Hello, I have a few questions, regarding the smoothScatter function. I have a scatter plot with more than 500,000 data points for two samples. So, I am wanting to display the density in colors to convince people that my good correlation coefficient is not due to an "influential point effect" and plus, I also want to make my scatter plot look pretty. Anyway ... I have been able to

Hi all. I am currently building a small test cloud based on Eucalyptus 2.0.3 and CentOS 5.8 x64. I have a choice which hypervisor to use: KVM or XEN. KVM is the default in CentOS 6 but I have read also many good things (for example PV guest machines, isolation between Dom0 and DomU) about XEN. Key factors from my opint of view are: - stability (which one runs more smoothly on CentOS?) -

Hi all. I am currently building a small test cloud based on Eucalyptus 2.0.3 and CentOS 5.8 x64. I have a choice which hypervisor to use: KVM or XEN. KVM is the default in CentOS 6 but I have read also many good things (for example PV guest machines, isolation between Dom0 and DomU) about XEN. Key factors from my opint of view are: - stability (which one runs more smoothly on CentOS?) -

Hi, I want to make a plot similar to sm1 (attached). The code I tried is: dcols <- densCols(x,y) smoothScatter(x,y, col = dcols, pch=20,xlab="A",ylab="B") abline(h=0, col="red") But it turned out to be s1 (attached) with big dots. I was wondering if anything wrong with my code. Thanks,Zhengyu -------------- next part -------------- A non-text

Hi! ? I have several mbox files (unstructured, only the big?plain mbox?files) and I would like to integrate/read these mbox files into mdbox subfolders of specific users.? This is a production system and I don't want to disturb running services and I am a bit afraid of not using the right command and maybe destroying (or whatever) their mailboxes.? I am sure it is pretty easy.? Can someone

Hi to everybody I have an array with dimensions 2,4,3,3. Wanting to sum the matrices in the first two dimensions, I'm trying to use the "apply" function, but with no results. Have to say I'm quite new with R syntax. tx in advance marco [[alternative HTML version deleted]]

Dear all, I have a dataset representing several geographical points (locations), each one having a specific value after performing a PCA. Now I'm trying to understand how to separately represent on a geographic map (of Europe) the pattern of PC1,2, ecc.with colors (e.g.heatmap) I have to add that the dataset includes relatively few points. Therefore, I would also like to interpolate the values

Good morning, I am analyzing a dataset composed by 364 subjects and 13 binary variables (0,1 = absence,presence). I am testing possible association (co-presence) of my variables. To do this, I was trying with cluster analysis. My main interest is to check for the significance of the obtained clusters. First, I tried with the pvclust() function, by using method.hclust="ward" and

Dear all, I'm analyzing a dataset (A) of 400 cases with 11 binary variables. Unfortunately, several (actually a lot) of cases are identical. NA are also present. I want to to plot distances between cases. For this, I obtained a distance matrix by dist(A, method="binary"). I then analyzed the obtained distance via Principal coordinate analysis with cmdscale(). Results are fine.