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

> 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 around with some different analyses and graphs. I

> attached a file headache.txt with all the R script (including the data

> in an Rish format). It is really a script file (i.e., .R) but for the

> listservs sake I saved it as a txt. There are quite a few different

> things I tried in there so hopefully it gives you some ideas.

> Regardless of the analysis type used and whether one considers

> proportion that "significantly improved" or the raw frequency or

> intensity scores, I would say that concluding the treatment was

> effective is a good conclusion. The only real concern could be that

> people would naturally get better on their own (a control group would

> be needed to bolster the causal inference drawn from a pre/post

> measurement). However, given at least what I know about migraines, it

> is often a fairly chronic condition so over a relatively short time

> period, it seems implausible to conclude that as many people would be

> improving as this study reported.

>

> Cheers,

>

> Josh

>

> On Sat, Nov 19, 2011 at 7:43 PM, Colstat <

[hidden email]> wrote:

> > hey, Joshua

> > I was reading this paper, in attachment, and reproducing the results.

> >

> > I was really confused when he said in the paper "The results were then

> > statistically analyzed using binomial distribution z statistics with

> > continuity correction." The data is binomial? To me, this is a paired

> > t-test.

> >

> > What command should I use to get those results (the first paragraph in

> > Results section)? Basically, it's a pre and post treatment problem.

> >

> > What other graphical analysis do you think is appropriate? reshape

> package?

> > lattice package, namely conditional graph?

> >

> > I know this might be too much, but I do really appreciate it if you do

> take

> > a look at it.

> >

> > Thanks,

> > Colin

> >

> >

> > On Sat, Nov 19, 2011 at 10:15 PM, Joshua Wiley <

[hidden email]>

> > wrote:

> >>

> >> 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 article? What did the article do?

> >> What you mention---some sort of z statistic (what exactly this was of

> >> and how it should be calculated did not seem like was clear even to

> >> you), histogram, t-test, lattice, are all very different things that

> >> help answer different questions, show different things, and in one is

> >> a piece of software.

> >>

> >> Without a clearer question and goal, my best advice is here are a

> >> number of different functions some of which may be useful to you:

> >>

> >> ls(pos = "package:stats")

> >>

> >> Cheers,

> >>

> >> Josh

> >>

> >> On Sat, Nov 19, 2011 at 3:01 PM, Colstat <

[hidden email]> wrote:

> >> > 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 7 NR

> >> > 7 38 F A 5 NA 10 2 9 SD

> >> > 8 44 M A 4 4 10 0 0 E

> >> > 9 47 M A 4 5 8 2 7 SD

> >> > 10 53 F A 5 3 10 0 0 E

> >> > 11 41 F N 5 6 7 0 0 E

> >> > 12 49 F A 4 6 8 0 0 E

> >> > 13 48 F A 5 4 8 0 0 E

> >> > 14 63 M N 4 6 9 15 9 NR

> >> > 15 58 M N 5 9 7 2 8 SD

> >> > 16 53 F A 4 3 9 0 0 E

> >> > 17 47 F N 5 4 8 1 4 SD

> >> > 18 34 F A NA 5 9 0 0 E

> >> > 19 53 F N 5 4 9 5 7 NR

> >> > 20 45 F N 5 5 8 5 4 SD

> >> > 21 30 F A 5 3 8 0 0 E

> >> > 22 29 F A 4 5 9 0 0 E

> >> > 23 49 F N 5 9 10 0 0 E

> >> > 24 24 F A 5 5 9 0 0 E

> >> > 25 63 F N 4 19 7 10 7 NR

> >> > 26 62 F A 5 8 9 11 9 NR

> >> > 27 44 F A 5 3 10 0 0 E

> >> > 28 38 F N 4 8 10 1 3 SD

> >> > 29 38 F N 5 3 10 0 0 E

> >> >

> >> > How do I do a binomial distribution z statistics with continuity

> >> > correction? basically normal approximation.

> >> > Could anyone give me some suggestions what I (or R) can do with these

> >> > data?

> >> > I have tried tried histogram, maybe t-test? or even lattice? what

> else

> >> > can

> >> > I(or can R) do?

> >> > help please, thanks so much.

> >> >

> >> > [[alternative HTML version deleted]]

> >> >

> >> > ______________________________________________

> >> >

[hidden email] mailing list

> >> >

https://stat.ethz.ch/mailman/listinfo/r-help> >> > PLEASE do read the posting guide

> >> >

http://www.R-project.org/posting-guide.html> >> > and provide commented, minimal, self-contained, reproducible code.

> >> >

> >>

> >>

> >>

> >> --

> >> Joshua Wiley

> >> Ph.D. Student, Health Psychology

> >> Programmer Analyst II, ATS Statistical Consulting Group

> >> University of California, Los Angeles

> >>

https://joshuawiley.com/> >

> >

>

>

>

> --

> Joshua Wiley

> Ph.D. Student, Health Psychology

> Programmer Analyst II, ATS Statistical Consulting Group

> University of California, Los Angeles

>

https://joshuawiley.com/>