#x27;s a little like early factor analysis when rotate the factors actually meant
rotate the glass plates.
--- On Sun, 11/20/11, Colstat wrote:
From: Colstat
Subject: Re: [R] Data analysis: normal approximation for binomial
To: "John Kane"
Cc: r-help@r-project.org
Received: Sunday, No
&hl=en&ei=nQHJTo7LPIrf0gHxs6Aq&sa=X&oi=book_result&ct=result&resnum=2&ved=0CC0Q6AEwAQ#v=onepage&q=z-test%20with%20continuity%20correction&f=false
>
> A print source that, IIRC, has a discussion of this is "Hayes, W. (1981.
> Statistics. 3rd Ed., Holt
Hey, Joshua
Thank so much for your quick response. Those examples you produced are
very good, I'm pretty impressed by the graphs. When I ran the last line, I
hit an error, so I ran what's inside summary(), it give me
Error: could not find function "lmer"
Something with the package "lme4"?
Colin
wAQ#v=onepage&q=z-test%20with%20continuity%20correction&f=false
A print source that, IIRC, has a discussion of this is "Hayes, W. (1981.
Statistics. 3rd Ed., Holt Rinehart and Winston
Have fun
--- On Sat, 11/19/11, Colstat wrote:
> From: Colstat
> Subject: [R] Data analysis: nor
Hi Colin,
I have never heard of a binomial distribution z statistic with (or
without for that matter) a continuity correction, but I am not a
statistician. Other's may have some ideas there. As for other ways
to analyze the data, I skimmed through the article and brought the
data and played arou
Hi,
I am not clear what your goal is. There is a variety of data there.
You could look at t-test differences in preIntensity broken down by
sex, you could use regression looking at postIntensity controlling for
preIntensity and explained by age, you could
Why are you analyzing data from an a
Dear R experts,
I am trying to analyze data from an article, the data looks like this
Patient Age Sex Aura preCSM preFreq preIntensity postFreq postIntensity
postOutcome
1 47 F A 4 6 9 2 8 SD
2 40 F A/N 5 8 9 0 0 E
3 49 M N 5 8 9 2 6 SD
4 40 F A 5 3 10 0 0 E
5 42 F N 5 4 9 0 0 E
6 35 F N 5 8 9 12
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