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
u110 5N/A
u2 6 N/A 4
u3 5 2
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
u110 5N/A
u2 6 N/A 4
u3 5
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
Hello,
In package qualityTools you can find one way to perform this analysis
through the gageRR() function.
The effect of an operator on the mesasurement system (Reproductibility) is
to me equivalent to the effect you try to study of your users when they are
in different groups.
Regards,
Carlos
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)
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
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,
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
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 glm(...,
family = poisson) instead of lm(...), where the ... is the stuff I gave
you before. A backup
Hi
I would try either some tree method (mvpart) or you can expand lm model
also with users.
fit-lm(value~variable+users, data=test.m)
Anyway I am not an ultimate expert in statistics. so you shall also
consult some appropriate literature which can be found in CRAN web. Did
you try to look
Hi
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
glm(...,
family = poisson) instead of lm(...), where the ... is the stuff I
gave
you before.
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
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