Hi Bert, The real situation is like what you suggested, user x group interactions. The users can be in more than one group. In fact, the data that I am trying to analyse constitute of users, online forums as groups and the attribute under measure is the number of posts made by each user in a particular forum.
My hypothesis is that the number of posts a user makes to a forum is dependent on the forum. For example if the user is in a forum that is active he contributes more compared to when he is in a forum that is less active. I guess there will be some users who contribute the same irrespective of the forum. I hope this makes sense. Regards Gawesh On Mon, Oct 10, 2011 at 4:50 PM, Bert Gunter <gunter.ber...@gene.com> wrote: > Yes, of course. But then one gets into additional problems with carryover > effects,etc. > Also, one then has a repeated measures problem (User is the experimental > unit) and my previous advice is nonsense, > > Like you, I have no idea what his real situation is. > > -- Bert > > > On Mon, Oct 10, 2011 at 8:39 AM, Anupam <anupa...@gmail.com> wrote: > >> It is possible to give multiple treatments, one at a time, to same pool of >> patients. You are correct that interactions may be important in this >> problem. I am only trying to help him frame the problem using an analogy. >> **** >> >> ** ** >> >> Anupam.**** >> >> *From:* Bert Gunter [mailto:gunter.ber...@gene.com] >> *Sent:* Monday, October 10, 2011 8:21 PM >> *To:* Anupam >> *Cc:* gj >> *Subject:* Re: [R] help with statistics in R - how to measure the effect >> of users in groups**** >> >> ** ** >> >> If that is the case, and each user can appear in only one group, there is >> no group x user interaction, the poster's question was nonsense, and one >> analyzes the group effect only, as originally shown >> >> -- Bert**** >> >> On Mon, Oct 10, 2011 at 7:43 AM, Anupam <anupa...@gmail.com> wrote:**** >> >> Groups are different treatments given to Users for your Outcome >> (measurement) of interest. Take this idea forward and you will have an >> answer. >> >> Anupam. >> -----Original Message----- >> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] >> On >> Behalf Of Bert Gunter >> Sent: Monday, October 10, 2011 7:36 PM >> To: gj >> Cc: r-help@r-project.org >> Subject: Re: [R] help with statistics in R - how to measure the effect of >> users in groups >> >> Assuming your data are in a data frame, yourdat, as: >> >> User Group Value >> u1 1 !0 >> u2 2 5 >> u3 3 NA >> ...(etc) >> >> where Group is **explicitly coerced to be a factor,** then you want the >> User >> x Group interaction, obtained from >> >> lm( Value ~ Group*User,data = yourdat) >> >> However, you'll get some kind of warning message if >> >> a) Not all Group x User combinations are present in the data >> >> b) Moreover, no statistics can be calculated if there are no replicates of >> UserxGroup combinations. >> >> If you do not know why either of these are the case, get local help or >> study >> any linear models (regression) text or online tutorial, as these last >> issues >> have nothing to do with R. >> >> -- Bert >> >> >> On Mon, Oct 10, 2011 at 3:48 AM, gj <gaw...@gmail.com> wrote: >> >> > Thanks Petr. I will try it on the real data. >> > >> > But that will only show that the groups are different or not. >> > Is there any way I can test if the users are different when they are >> > in different groups? >> > >> > Regards >> > Gawesh >> > >> > On Mon, Oct 10, 2011 at 11:17 AM, Petr PIKAL <petr.pi...@precheza.cz> >> > wrote: >> > >> > > > >> > > > Hi Petr, >> > > > >> > > > It's not an equation. It's my mistake; the * are meant to be field >> > > > separators for the example data. I should have just use blank >> > > > spaces as >> > > > follows: >> > > > >> > > > users Group1 Group2 Group3 >> > > > u1 10 5 N/A >> > > > u2 6 N/A 4 >> > > > u3 5 2 3 >> > > > >> > > > >> > > > Regards >> > > > Gawesh >> > > >> > > OK. You shall transform your data to long format to use lm >> > > >> > > test <- read.table("clipboard", header=T, na.strings="N/A") >> > > test.m<-melt(test) >> > > Using users as id variables >> > > fit<-lm(value~variable, data=test.m) >> > > summary(fit) >> > > >> > > Call: >> > > lm(formula = value ~ variable, data = test.m) >> > > >> > > Residuals: >> > > 1 2 3 4 6 8 9 >> > > 3.0 -1.0 -2.0 1.5 -1.5 0.5 -0.5 >> > > >> > > Coefficients: >> > > Estimate Std. Error t value Pr(>|t|) >> > > (Intercept) 7.000 1.258 5.563 0.00511 ** >> > > variableGroup2 -3.500 1.990 -1.759 0.15336 >> > > variableGroup3 -3.500 1.990 -1.759 0.15336 >> > > --- >> > > Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 >> > > >> > > Residual standard error: 2.179 on 4 degrees of freedom >> > > (2 observations deleted due to missingness) >> > > Multiple R-squared: 0.525, Adjusted R-squared: 0.2875 >> > > F-statistic: 2.211 on 2 and 4 DF, p-value: 0.2256 >> > > >> > > No difference among groups, but I am not sure if this is the correct >> > > way to evaluate. >> > > >> > > library(ggplot2) >> > > p<-ggplot(test.m, aes(x=variable, y=value, colour=users)) >> > > p+geom_point() >> > > >> > > There is some sign that user3 has lowest value in each group. >> > > However for including users to fit there is not enough data. >> > > >> > > Regards >> > > Petr >> > > >> > > >> > > > >> > > > >> > > > On Mon, Oct 10, 2011 at 9:32 AM, Petr PIKAL >> > > > <petr.pi...@precheza.cz> >> > > wrote: >> > > > >> > > > > Hi >> > > > > >> > > > > I do not understand much about your equations. I think you shall >> > > > > look >> > > to >> > > > > Practical Regression and Anova Using R from J.Faraway. >> > > > > >> > > > > Having data frame DF with columns - users, groups, results you >> > > > > could >> > > do >> > > > > >> > > > > fit <- lm(results~groups, data = DF) >> > > > > >> > > > > Regards >> > > > > Petr >> > > > > >> > > > > >> > > > > >> > > > > >> > > > > > >> > > > > > Hi, >> > > > > > >> > > > > > I'm a newbie to R. My knowledge of statistics is mostly >> > self-taught. >> > > My >> > > > > > problem is how to measure the effect of users in groups. I can >> > > calculate >> > > > > a >> > > > > > particular attribute for a user in a group. But my hypothesis >> > > > > > is >> > > that >> > > > > the >> > > > > > user's attribute is not independent of each other and that the >> > > user's >> > > > > > attribute depends on the group ie that user's behaviour change >> > based >> > > on >> > > > > the >> > > > > > group. >> > > > > > >> > > > > > Let me give an example: >> > > > > > >> > > > > > users*Group 1*Group 2*Group 3 >> > > > > > u1*10*5*n/a >> > > > > > u2*6*n/a*4 >> > > > > > u3*5*2*3 >> > > > > > >> > > > > > For example, I want to be able to prove that u1 behaviour is >> > > different >> > > > > in >> > > > > > group 1 than other groups and the particular thing about Group >> > > > > > 1 is >> > > that >> > > > > > users in Group 1 tend to have a higher value of the attribute >> > > > > > under measurement. >> > > > > > >> > > > > > >> > > > > > Hence, can use R to test my hypothesis. I'm willing to learn; >> > > > > > so if >> > > this >> > > > > is >> > > > > > very simple, just point me in the direction of any online >> > > > > > resources >> > > > > about >> > > > > > it. At the moment, I don't even how to define these class of >> > > problems? >> > > > > That >> > > > > > will be a start. >> > > > > > >> > > > > > Regards >> > > > > > Gawesh >> > > > > > >> > > > > > [[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. >> > > > > >> > > > > >> > > > >> > > > [[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. >> > > >> > > >> > >> > [[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. >> > >> > >> >> [[alternative HTML version deleted]] >> >> **** >> >> ** ** >> > > > > -- > "Men by nature long to get on to the ultimate truths, and will often be > impatient with elementary studies or fight shy of them. If it were possible > to reach the ultimate truths without the elementary studies usually prefixed > to them, these would not be preparatory studies but superfluous diversions." > > -- Maimonides (1135-1204) > > Bert Gunter > Genentech Nonclinical Biostatistics > 467-7374 > > http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm > > > [[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.
