help with statistics in R - how to measure the effect of users in groups

OK. So my original advice and warnings are correct.

However, now there is an additional wrinkle because your response is a
count, which is not a continuous measurement. For this, you'll need 

family = "poisson") instead of lm(...), where the ... is the stuff I 

you before. A backup approach is there aren't too many small counts 

about 10, say) is to take the square root of the counts and analyze that 

lm().

In either approach, your interpretation becomes more difficult -- e.g. 

you any experience with glm's = generalized linear models? Moreover, if
there are large numbers of users -- e.g. > dozens (and you may have 

or thousands -- of course the interaction will be significant, but so 

For this you'll need to re-frame the question.

So given all this and what appears to be your relative ignorance of
statistics, I strongly recommend that you get local statistical help. Or
just forget about formal statistical analysis altogether and do some
sensible plotting.

library(ggplot2)
p<-ggplot(test.m, aes(x=variable, y=value, colour=users))
p+geom_point()

Finally, that's it for me on this. I will offer you no more advice.

-- Bert

On Mon, Oct 10, 2011 at 9:40 AM, gj <gawesh at gmail.com> wrote:

Hi Bert,

The real situation is like what you suggested, user x group 

The users can be in more than one group.
In fact, the data that I am trying to analyse constitute of users, 

forums as groups and the attribute under measure is the number of 

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 

he contributes more compared to when he is in a forum that is less 

guess there will be some users who contribute the same irrespective of 

forum.

I hope this makes sense.

Regards
Gawesh

On Mon, Oct 10, 2011 at 4:50 PM, Bert Gunter 

Yes, of course. But then one gets into additional problems with 

effects,etc.
Also, one then has a repeated measures problem (User is the 

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 <anupamtg at gmail.com> wrote:

It is possible to give multiple treatments, one at a time, to same 

of patients. You are correct that interactions may be important in 

problem. I am only trying to help him frame the problem using an 

****

** **

Anupam.****

*From:* Bert Gunter [mailto:gunter.berton at 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 

of users in groups****

** **

If that is the case, and each user can appear in only one group, 

no group x user interaction, the poster's question was nonsense, and 

analyzes the group effect only, as originally shown

-- Bert****

On Mon, Oct 10, 2011 at 7:43 AM, Anupam <anupamtg at gmail.com> 

Groups are different treatments given to Users for your Outcome
(measurement) of interest. Take this idea forward and you will have 

answer.

Anupam.
-----Original Message-----
From: 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 

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 

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 

of
UserxGroup combinations.

If you do not know why either of these are the case, get local help 

study
any linear models (regression) text or online tutorial, as these 

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 

in different groups?

Regards
Gawesh

On Mon, Oct 10, 2011 at 11:17 AM, Petr PIKAL 

wrote:

Hi Petr,

It's not an equation. It's my mistake; the * are meant to be 

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 

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 

look

to

Practical Regression and Anova Using R from J.Faraway.

Having data frame DF with columns - users, groups, results 

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 

calculate

a

particular attribute for a user in a group. But my 

is

that

the

user's attribute is not independent of each other and that 

user's

attribute depends on the group ie that user's behaviour 

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 

different

in

group 1 than other groups and the particular thing about 

1 is

that

users in Group 1 tend to have a higher value of the 

under measurement.


Hence, can use R to test my hypothesis. I'm willing to 

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 

problems?

That

will be a start.

Regards
Gawesh

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****

** **

--
"Men by nature long to get on to the ultimate truths, and will often 

impatient with elementary studies or fight shy of them. If it were 

to reach the ultimate truths without the elementary studies usually 

to them, these would not be preparatory studies but superfluous 

-- Maimonides (1135-1204)

Bert Gunter
Genentech Nonclinical Biostatistics
467-7374

biostatistics/pdb-ncb-home.htm

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