I should have added...

If your design is not "nearly" balanced, main effects and interactions will
not have any "natural" interpretation because they will be (partially)
confounded. (I realize "nearly" is not a very useful characterization, but I
do not know a better one, as it probably depends on the scientific context
of your data).

Again, if you do not know what this means, get statistical help as I
previously suggested. Or you might want to try the stats.stackexchange.com
website.

-- Bert

On Mon, Oct 10, 2011 at 7:06 AM, Bert Gunter <bgunter@gene.com> wrote:

> 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 <gawesh@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.pikal@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.pikal@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.
>>
>>
>

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