PROTECTED] , to the attention of
Jim Rogers.
James A. Rogers, Ph.D. <[EMAIL PROTECTED]>
Statistical Scientist
Cantata Pharmaceuticals
300 Technology Square, 5th floor
Cambridge, MA 02139
617.225.9009 x312
Fax 617.225.9010
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Please note Student-Newman-Keuls is NOT a recommended multiple comparison procedure.
In the language of Hsu (1996), Student-Newman-Keuls is not even a "confident
inequalities" method. In other words, it does not control the probability of making at
least one incorrect assertion of inequality (wh
>>> I'm wondering if anyone has written some functions or code for
>>> handling
>>> very large files in R. I am working with a data file that is 41
>>> variables times who knows how many observations making up 27MB
altogether.
>>>
>>> The sort of thing that I am thinking of having R do is
>>>
everything else). It works with data.frames
with factor and numeric columns. Character columns need to be coverted
to factor.
It sounds like this code will be available in the next build of Rggobi
for Windows, which Duncan informs me is in the not too distant future.
Thanks, Duncan!
Jim
> Jim Rog
looks easy to write such a function to handle data.frames with only
numeric data, but a bit of work with character and factor data.
Thanks,
Jim Rogers
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Just one more thing for you to stew on...
Happy thinking,
Jim Rogers
> Hello to all: first and foremost: thank you for all this input. I only
discovered about "R" last week (!) and I think I will dump my SAS
license!!!
>
>
> ;-)
>
>
> This is a very dynami
2
I was first confused by this a few months back, and Luke Tierney, Robert
Gentleman, and Thomas Lumley were kind enough to explain it to me. If
you want to find that thread, do an R site search for "lexical scoping
Jim Rogers".
Cheers,
Jim
> Message: 43
> Date: Mon, 21 Jul 200
# 5 (Why not 4 ?)
fs[[2]](3) # 5
Checking the environments of these functions, I see that "y" is indeed
bound to the value 2 in both cases:
es <- lapply(fs, environment)
ys <- lapply(es, function(env) get("y", env)) # list(2, 2)
?
Thanks for help,
Jim Rogers
James
The options I know of are:
1. aggregate (in the base package), with FUN = length. But this converts
character vectors to factors, which is sometimes annoying and sometimes
dangerous.
2. summarize, in the Hmisc package (again, with FUN = length). I find
summarize to be a very useful function in gen
