Em 9/9/2011 14:32, array chip escreveu:
Thanks all again for the suggestions. I agree with Wolfgang that
mcnemar.test() is what I am looking for. The accuracy is the
proportion of correct diagnosis compared to a gold standard, and I am
interested in which diagnosis test is better, not particular
between the 2
tests.
Thanks again.
John
- Original Message -
From: Viechtbauer Wolfgang (STAT)
To: "r-help@r-project.org"
Cc: csrabak ; array chip
Sent: Thursday, September 8, 2011 1:24 AM
Subject: RE: [R] suggestion for proportions
I assume you mean Cohen's kappa. T
Em 8/9/2011 05:24, Viechtbauer Wolfgang (STAT) escreveu:
I assume you mean Cohen's kappa. This is not what the OP is asking
about. The OP wants to know how to test for a difference in the
proportions of 1's. Cohen's kappa will tell you what the level of
agreement is between the two tests. This is
t.org]
> On Behalf Of csrabak
> Sent: Thursday, September 08, 2011 02:31
> To: array chip
> Cc: r-help@r-project.org
> Subject: Re: [R] suggestion for proportions
>
> Em 7/9/2011 16:53, array chip escreveu:
> > Hi all, thanks very much for sharing your thoughts. and sorry
Correction. It won't work. Please ignore.
>>> John Sorkin 9/7/2011 10:41:46 PM >>>
Let my try again, but this time with corrected R code:
would the following strategy work:
numtests <- 2000
# Create a data frame: test1 results from trial 1
# test2 results from trial 2
#
Let my try again, but this time with corrected R code:
would the following strategy work:
numtests <- 2000
# Create a data frame: test1 results from trial 1
# test2 results from trial 2
# agree indicagtor if trial1= trial2 (value =1) or
#
Would the following strategy work?
numtests <- 20
# Create a data frame: test1 results from trial 1
# test2 results from trial 2
# agree indicagtor if trial1= trial2 (value =1) or
# trial1<>trial2 (value =0)
data <-
Em 7/9/2011 16:53, array chip escreveu:
Hi all, thanks very much for sharing your thoughts. and sorry for my describing
the problem not clearly, my fault.
My data is paired, that is 2 different diagnostic tests were performed on the same
individuals. Each individual will have a test results fr
nemar.test() is good for that, right?
Thanks
John
- Original Message -
From: Viechtbauer Wolfgang (STAT)
To: "r-help@r-project.org"
Cc: Bert Gunter
Sent: Wednesday, September 7, 2011 8:14 AM
Subject: Re: [R] suggestion for proportions
Indeed, the original post
September 07, 2011 16:47
> To: Viechtbauer Wolfgang (STAT)
> Cc: r-help@r-project.org; John Sorkin
> Subject: Re: [R] suggestion for proportions
>
> Wolfgang:
>
> On Wed, Sep 7, 2011 at 7:28 AM, Viechtbauer Wolfgang (STAT)
> wrote:
> > Acutally,
> >
>
gt;
> since it is paired data.
>
> Best,
>
> Wolfgang
>
>> -Original Message-
>> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
>> On Behalf Of Bert Gunter
>> Sent: Wednesday, September 07, 2011 15:34
>> To: John Sork
Message-
>> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
>> On Behalf Of Bert Gunter
>> Sent: Wednesday, September 07, 2011 15:34
>> To: John Sorkin
>> Cc: r-help@r-project.org
>> Subject: Re: [R] suggestion for proportions
>>
>
ject.org
> Subject: Re: [R] suggestion for proportions
>
> Please! ... ?prop.test
>
> not t tests.
>
> -- Bert
>
> --
>
> On Wed, Sep 7, 2011 at 4:21 AM, John Sorkin
> wrote:
> > >From you description, you should not used a paired Student's t
Please! ... ?prop.test
not t tests.
-- Bert
--
On Wed, Sep 7, 2011 at 4:21 AM, John Sorkin wrote:
> >From you description, you should not used a paired Student's t-test. One
> >uses a paired test when pairs of observations come from the same
> >experimental unit (and thus are correlated).
>From you description, you should not used a paired Student's t-test. One uses
>a paired test when pairs of observations come from the same experimental unit
>(and thus are correlated). You describe a study where each experimental unit
>is tested once and where there are two independent groups o
Hi, I am wondering if anyone can suggest how to test the equality of 2
proportions. The caveat here is that the 2 proportions were calculated from the
same number of samples using 2 different tests. So essentially we are comparing
2 accuracy rates from same, say 100, samples. I think this is lik
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