> > 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. ______________________________________________ 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.