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. |
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/ ______________________________________________ [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. |
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/ ______________________________________________ [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. headache.txt (9K) Download Attachment |
In reply to this post by colstat
Hi Colin,
I'm no statistician and it's been a very long time but IIRC a t-test is a 'modified version of a x-test that is used on small sample sizes. (I can hear some of our statistians screaming in the background as I type.) In any case I thing a Z distribution is descrete and a standard normal is not so a user can use Yates continuity correction to interpolate values for the normal between the discrete z-values. Or something like this. I have only encountered it once in a Psych stats course taught by an animal geneticist who seemed to think it was important. To be honest, it looked pretty trivial for the type of data I'd be likely to see. I cannot remember ever seeing a continuity correction used in a published paper--for that matter I have trouble remembering a z-test. If you want more information on the subject I found a very tiny bit of info at http://books.google.ca/books?id=SiJ2UB3dv9UC&pg=PA139&lpg=PA139&dq=z-test+with+continuity+correction&source=bl&ots=0vMTCUZWXx&sig=bfCPx0vynGjA0tHLRAf6B42x0mM&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 Rinehart and Winston Have fun --- On Sat, 11/19/11, Colstat <[hidden email]> wrote: > From: Colstat <[hidden email]> > Subject: [R] Data analysis: normal approximation for binomial > To: [hidden email] > Received: Saturday, November 19, 2011, 6:01 PM > 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. > ______________________________________________ [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. |
In reply to this post by Joshua Wiley-2
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 <[hidden email]>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 <[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/ > [[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. |
In reply to this post by John Kane-2
Hey, John
I like the explicit formula they put in there. I looked around last night and found this http://www.stat.yale.edu/Courses/1997-98/101/binom.htm which is basically normal approximation to the binomial, I thought that was what the author was trying to get at? Colin On Sun, Nov 20, 2011 at 8:49 AM, John Kane <[hidden email]> wrote: > Hi Colin, > > I'm no statistician and it's been a very long time but IIRC a t-test is a > 'modified version of a x-test that is used on small sample sizes. (I can > hear some of our statistians screaming in the background as I type.) > > In any case I thing a Z distribution is descrete and a standard normal is > not so a user can use Yates continuity correction to interpolate values for > the normal between the discrete z-values. Or something like this. > > I have only encountered it once in a Psych stats course taught by an > animal geneticist who seemed to think it was important. To be honest, it > looked pretty trivial for the type of data I'd be likely to see. > > I cannot remember ever seeing a continuity correction used in a published > paper--for that matter I have trouble remembering a z-test. > > If you want more information on the subject I found a very tiny bit of > info at > http://books.google.ca/books?id=SiJ2UB3dv9UC&pg=PA139&lpg=PA139&dq=z-test+with+continuity+correction&source=bl&ots=0vMTCUZWXx&sig=bfCPx0vynGjA0tHLRAf6B42x0mM&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 Rinehart and Winston > > Have fun > > --- On Sat, 11/19/11, Colstat <[hidden email]> wrote: > > > From: Colstat <[hidden email]> > > Subject: [R] Data analysis: normal approximation for binomial > > To: [hidden email] > > Received: Saturday, November 19, 2011, 6:01 PM > > 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. > > > [[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. |
You need a statistician or at least someone who's take a stats course in the last 10 years but it may be what the author was trying to get at.
At least the binomial is descrete as is the z so it may be that the z was used as easier to calculate than a binomial? How old is the paper. Before, let's say the early 1980s a lot of people were still doing stats by hand (IIRC, even calculators were relatively expensive and rare and calculating a binomial of any size was close to impractical.-- I tried it once). So using a z-distribution with Yates correction made sense. It's a little like early factor analysis when rotate the factors actually meant rotate the glass plates. --- On Sun, 11/20/11, Colstat <[hidden email]> wrote: From: Colstat <[hidden email]> Subject: Re: [R] Data analysis: normal approximation for binomial To: "John Kane" <[hidden email]> Cc: [hidden email] Received: Sunday, November 20, 2011, 10:10 PM Hey, John I like the explicit formula they put in there. I looked around last night and found this http://www.stat.yale.edu/Courses/1997-98/101/binom.htm which is basically normal approximation to the binomial, I thought that was what the author was trying to get at? Colin On Sun, Nov 20, 2011 at 8:49 AM, John Kane <[hidden email]> wrote: Hi Colin, I'm no statistician and it's been a very long time but IIRC a t-test is a 'modified version of a x-test that is used on small sample sizes. (I can hear some of our statistians screaming in the background as I type.) In any case I thing a Z distribution is descrete and a standard normal is not so a user can use Yates continuity correction to interpolate values for the normal between the discrete z-values. Or something like this. I have only encountered it once in a Psych stats course taught by an animal geneticist who seemed to think it was important. To be honest, it looked pretty trivial for the type of data I'd be likely to see. I cannot remember ever seeing a continuity correction used in a published paper--for that matter I have trouble remembering a z-test. If you want more information on the subject I found a very tiny bit of info at http://books.google.ca/books?id=SiJ2UB3dv9UC&pg=PA139&lpg=PA139&dq=z-test+with+continuity+correction&source=bl&ots=0vMTCUZWXx&sig=bfCPx0vynGjA0tHLRAf6B42x0mM&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 Rinehart and Winston Have fun --- On Sat, 11/19/11, Colstat <[hidden email]> wrote: > From: Colstat <[hidden email]> > Subject: [R] Data analysis: normal approximation for binomial > To: [hidden email] > Received: Saturday, November 19, 2011, 6:01 PM > 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. > [[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. |
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