Hi Matt, see the example below. It took me a while to figure it out. I
suggest you carefully examine the example step by step. It computes t-values
for dataset with 3 variables and 8 unique combinations of two binning
variables. The code should extend easily to larger datasets. Also, it uses
the e
help@r-project.org
> Subject: Re: [R] multiple paired t-tests without loops
>
> Yes, I suspect that I will end up using a sampling approach, but I'd
> like to use an exact test if it's at all feasible.
>
> Here are two samples of data from 3 subjects:
> Sample
Hi Matthew,
First - I fully support Greg Snow proposition. Sampling is the way to go
here.
But besides that:
1) Try to avoid using data.frames as much as possible (use vectors and
matrixes instead - they are usually faster)
2) Since you are running on a loop, you can try running it in parallel (
Yes, I suspect that I will end up using a sampling approach, but I'd
like to use an exact test if it's at all feasible.
Here are two samples of data from 3 subjects:
Sample SubjC1 C2
44 1 0.0093 0.0077
44 2 0.0089 0.0069
44 3 0.051 0.0432
44 4
801.408.8111
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
> project.org] On Behalf Of Matthew Finkbeiner
> Sent: Saturday, April 24, 2010 4:58 AM
> To: r-help@r-project.org
> Subject: [R] multiple paired t-tests without loops
>
I am new to R and I suspect my problem is easily solved, but I haven't
been able to figure it out without using loops. I am trying to
implement Blair & Karniski's (1993) permutation test. I've included a
sample data frame below. This data frame represents the conditional
means (C1, C2) for 3
I am new to R and I suspect my problem is easily solved, but I haven't
been able to figure it out without using loops. I am trying to
implement Blair & Karniski's (1993) permutation test. I've included a
sample data frame below. This data frame represents the conditional
means (C1, C2) for 3
I'm not really looking for a needle in a haystack, there are a small
number of the 60 tests (about 20) that are likely to concord with other
experiments I have, and in a particular pattern. Since I already have
the data in tables for graphic depiction, I was hoping to have a
reasonably easy way to
so you want to find a needle in a haystack, not an easy task. You should
account for multiple tests, which is as far as I can see not done in the
code yet - or you have to accept that you find a bunch of hay which
accidentally looks pretty much like a needle.
There are some solutions in doing su
Perfect. In conjunction with Jorge's contrib that works a treat. Thanks.
Dan
On Tue, 2009-03-24 at 19:00 -0400, David Winsemius wrote:
> ?try
> ?tryCatch
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PLEASE do read t
That is a valid point, the number of samples I expect to be different
is actually quite small, but it is supportable (or otherwise) by
other experimental data.
Unfortunately the question I really want answered is pretty much
covered by doing this.
thanks
Dan
On 25/03/2009, at 10:25 AM,
.. and you will end up - in your example- with 60 t-statistics and
p-values (so you do bonforroni adjustment or something like that)?!
Sometimes the question for "How do I ..." should be read as "What is
the question I *really* want to be answered ...". You may consider doing
some more sophis
?try
?tryCatch
On Mar 24, 2009, at 6:04 PM, Dan Kortschak wrote:
Hi Jorge,
That is exactly what I wanted - I should have given a reasonable
number of observations (my set has *almost* all paired observations,
so it will still break with that approach unless I manicure the data
set). Is there
Hi Jorge,
That is exactly what I wanted - I should have given a reasonable
number of observations (my set has *almost* all paired observations,
so it will still break with that approach unless I manicure the data
set). Is there a way to fail nicely on a single one of the tests
without the
Hi R users,
I have a very large data set that has two conditioning variables for the
test I want to perform.
A toy set can be simulated:
type<-sample(1:3,100,replace=TRUE)
class<-sample(1:20,100,replace=TRUE)
value<-rnorm(100)
data<-cbind(type,class,value)
(though type and class are alphanum)
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