Date: Sat, 11 Jun 2011 21:52:21 -0400
From: ccoll...@purdue.edu
To: r-help@r-project.org
Subject: [R] smoothScatter function (color density question) and adding a
legend
Dear R experts,
I am resending my questions below one more time
just in case someone out there could help
but missed my email.
Thanks, I was curious about this and so I installed the geneplot package
and that went fine but I had another issue and need to take care of that.
First, see if ?legend helps at all ( no idea, I have not used this stuff
much).
Also source code should be available for the pacakge you care about.
On quick read, it sounds like this uses a binning system for colors. If you
want something slightly different and much slower, I have some examples
( IIRC) that calculate densities in R using something similar to electric
field calculation around points, ( again I'm in a hurry and pulling this
from archive so caveat emptor this plot may not correspond exactly to
example script below etc),
http://98.129.232.232/coloumb1.pdf
mydensity-function(x1,x2)
{
len=length(x1);
z-1:len;
for ( y in 1:len )
{
nx=c(1:(y-1),(y+1):len);
if ( y==1) {nx=2:len;}
if (y==len) {nx=1:(len-1); }
# coloumb is a bit much with overlapping data points, so limite it a bit
z[y]=sum(1.0/(.001*1+(x1[nx]-x1[y])*(x1[nx]-x1[y])+(x2[nx]-x2[y])*(x2[nx]-x2[y])));
#for
}
print(max(z));
print(min(z));
z=z-min(z);
z=100*z/max(z);
hist(z);
color=rainbow(100);
#color=heat.colors(10);
tmap=color[floor(z)+1];
scatterplot3d(x1,x2,z,color=tmap);
plot(x2,x1,col=tmap,cex=.5)
#library(VecStatGraphs2D)
#DrawDensityMap(x1,x2,PaintPoint=TRUE)
#color=terrain.colors(26)
#color=heat.colors(26)
}
I don't think my questions are too hard.
I am most concerned about the transformation function.
See below.
Thanks,
Clayton
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 make the plot, but I have a couple of questions about it.
I also want to make a legend for it, which I don't know how to do.
I have only been playing around with R for a few weeks now off and on.
So, I am definitely a beginner.
1.
I have 10 colors for my plot: white representing zero density and dark red
representing the maximum density (I presume).
According to the R documentation, the transformation argument represents the
function mapping the density scale to the color scale.
Note that in my R code below, transformation = function(x) x^0.14.
I was wondering how exactly or mathematically that this function relates the
density to color.
I believe the colorRampPalette ramps colors between 0 and 1. I am not sure if
x represents the color or the
density. Since I have 10 colors, I suppose the colorRampPalette would assign
values of
0, 0.11, 0.22, 0.33, 0.44, 0.55, 0.66, 0.77, 0.88, and 1 for white to dark
red. I am not sure though.
Does anyone know how this works? I am sure its not too too complicated.
2.
In a related issue, I also would like to make a legend for this plot. Then, I
would be able to see the transformation
function's effects on the color-density relationship. Could someone help me
in making a legend for my smoothScatter plot? I would like to place it
immediately to the right of my plot as a vertical bar, matching the vertical
length of the plot as is often convention.
I really like the smoothScatter function. It is easy to use, and I believe
it's relatively new.
Thanks in advance.
-Clayton
clayton - c(white, lightblue, blue, darkgreen, green, yellow,
orange, darkorange, red, darkred)
x - read.table(c:/users/ccolling/log_u1m1.txt, sep =\t)
smoothScatter(x$V8~x$V7,
nbin=1000,
colramp = colorRampPalette(c(clayton)),
nrpoints=Inf,
pch=,
cex=.7,
transformation = function(x) x^.14,
col=black,
main=M1 vs. M3 (r = 0.92),
xlab=Normalized M1,
ylab=Normalized M2,
cex.axis = 1.75,
cex.lab = 1.25
cex.main = 2)
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
