[R] help with statistics in R - how to measure the effect of users in groups
Anupam
anupamtg at gmail.com
Mon Oct 10 16:43:57 CEST 2011
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-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Bert Gunter
Sent: Monday, October 10, 2011 7:36 PM
To: gj
Cc: r-help at 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 <gawesh at 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 at 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 at 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 at 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 at 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 at 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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