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 On Sun, Nov 20, 2011 at 1:00 AM, Joshua Wiley <jwiley.ps...@gmail.com>wrote: > 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 <cols...@gmail.com> 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 <jwiley.ps...@gmail.com> > > 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 <cols...@gmail.com> 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]] > >> > > >> > ______________________________________________ > >> > R-help@r-project.org 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/ > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org 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.
