If you make your calls to density with common lenth and interval
parameters you should be able to get better "registration":
?density
# this example sums the squared differences
x <- rnorm(200,1,1)
x2 <- rnorm(200,1,1)
d1 <- density(x, n=512, from=-1, to= 4)
d2 <- density(x2, n=512, from=-1, to= 4)
ssq <- sum( (d1$y - d2$y)^2 )
ssq
--
David Winsemius
On Mar 18, 2009, at 1:54 PM, Aaron Spivak wrote:
Hi,
This is my first time posting to the mailing list, so if I'm doing
something
wrong, just let me know. I've taken ~1000 samples from 8 biological
replicates, and I want to somehow combine the density functions of the
replicates. Currently, I can plot the density function for each
biological
replicate, and I'd like to see how pool of replicates compares to a
simulation I conducted earlier. I can compare each replicate to the
simulation, but there's a fair amount of variability between
replicates.
I'd like to take the geometric mean of the density functions at each
point
along the x-axis, but when I compute:
a<-density(A[,1][A[,1]>=0], n=2^15)
b<-density(A[,3][A[,3]>=0], n=2^15)
a$x[1]
[1] -70.47504
b$x[1]
[1] -69.28902
So I can't simply compute the mean across y-values, because the x-
values
don't match. Is there a way to set the x-values to be the same for
multiple
density plots? Also, there are no negative values in the dataset,
so I'd
like to bound the x-axis at 0 if at all possible? Is there a
standard way
to combine density functions? Thanks for the advice.
-Aaron Spivak
ps. I thought about just pooling all measurements, but I don't think
that's
appropriate because they are from different replicates and the
smoothing
kernel depends on the variance in the sample to calculate the
distribution.
[[alternative HTML version deleted]]
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
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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.
