On Aug 5, 2011, at 1:50 PM, Ken H wrote:
That's a good point Josh is correct,
Its the R Bible because its the size of the Bible and serves as a
very good
reference.
Some people apparently think so [assuming here that you are referring
to Crawley.] My experience is less favorable. When I
) is a good tool in base R and the rbenchmark package
makes it easy to compare various approaches.
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
> Behalf Of Bert Gunter
>
I think that Josh may have inadvertently left out Venables's and
Ripley"s MASS (the book), which is what I would choose if I were
marooned on a desert island with my computer, a power supply, and
unlimited mai tai's. Also Mozart's Piano Concerti if I had to limit my
music to just one composer ( :-)
That's a good point Josh is correct,
Its the R Bible because its the size of the Bible and serves as a very good
reference. I agree that it is definitely not a first blush kind of book. I
second the regression book, it is excellent.
Cryer and Chan Time Series Analysis with Applications in R is pret
Inline below.
On Fri, Aug 5, 2011 at 10:23 AM, R. Michael Weylandt
wrote:
> Bert,
>
> You are absolutely correct: I was wrong not to vectorize in this case.
>
No. That wasn't my point at all. In this case, vectorizing doesn't
seem to help because you still must do a loop (via *ply) in R. My
poin
Bert,
You are absolutely correct: I was wrong not to vectorize in this case.
I am surprised, however, by your remark that sapply() (or really lapply())
is faster than apply() -- is there a reason for this? I would have guessed
that the major difference between the two would have been memory manag
On Fri, Aug 5, 2011 at 9:20 AM, Ken H wrote:
[snip]
> And that should be it, as far as relevant reading
> Peter Daalgard's Introductory Statistics with R is very good if you do not
> know other programming languages.
I would strongly second this. It is a very nice book. What book to
read depe
Michael:
I'm sorry, but this "advice" is wrong. replicate() **IS** essentially
a loop: it uses sapply(), which is basically an interpreted loop (with
suitable caveats that R experts can provide).
The correct advice is: whenever possible, move the loops down to
underlying C code by vectorizing. In
Hey, no problem! We all have to start somewhere, welcome to R!
The structure of the for loop is as follows:
First lets define the number you want, say
>vector.size = 10
First allocate an empty vector to store the results, you could do this
like so
> V95.Vector<-c()
But I recommend you do
This is a textbook of when NOT to use a loop in R: rather make a function
that does what you want and use the replicate function to do it repeatedly.
f <- function(){
return(-1000*quantile(rnorm(250,0,0.2),0.95)
}
x = replicate(1e5,f())
There are your desired numbers.
Some general coding princi
Hi,
Can someone help me out to create a (for?) loop for the following procedure:
x=rnorm(250,0,0.02)
library(timeSeries)
x=timeSeries(x)
P=1000
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