Hi Johnny,
Something like this
rbind(NA, dat.med)[as.numeric(dat$image.group), ]
should do the trick (with the data you provided and Ista's code). The
key is that dat.med has a different row for each level of the factor
image.group (and in the same order). The idea is to convert the
factor cre
HI Ista,
Thanks for the help. The 'cut' function seems to do the trick .
I'm not sure why you suggested this line of code:
> ddply(dat, .(image.group), transform, measure.median = median(Measurement))
I think I might have confused the issue by putting a 'Measurement' column in my
example in th
Hi Johnny,
If I understand correctly, I think you can use cut() to create a grouping
variable, and then calculate your summaries based on that. Something like
dat <- read.csv("~/Downloads/exampledata.csv")
dat$image.group <- cut(dat$a.ImageNumber, breaks = seq(0,
max(dat$a.ImageNumber), by = 3))
Hi all,
Since I could not attach a file to my original e-mail request, for those who
want to look at an example of a data file I am working with, please use this
link:
http://dl.dropbox.com/u/4637975/exampledata.csv
Thanks again,
Johnny.
__
R-help
Hi there,
I hope you have time to read this question and offer a suggestion or two.
My basic question is this:
I have data in sets of three. I would like to combine the data from each set,
perform a function (probably just taking the median and MAD), then re-assign
these values to each of t
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