Thanks for your prompt responses. I will look at the readings you sugggest.
One quick question, sampling weights can be applied in clmm2?
Thank you,
Wander
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I forgot to add. How can I estimate cluster-robust standard errors and 95%
confidence intervals for odds ratios?
Thank you,
Wander
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Sent from the R help mailing list
-project.org] On
Behalf Of shkingdom
Sent: Saturday, 1 March 2014 11:57
To: r-help@r-project.org
Subject: [R] Multilevel analysis for ordinal responses
Dear all,
I need to fit a multielvel model for an ordinal response. Does R have a
command for conducting a multilevel ordinal logistic regression when
Yes; see clm and clmm2 (mixed effects) in the ordinal package for
fitting proportional odds models. See section 3 of
http://cran.r-project.org/web/packages/ordinal/vignettes/clm_tutorial.pdf
to see how to test the proportional odds assumption with clm - it is
equivalent for clmm2 models. For an
Dear all,
I need to fit a multielvel model for an ordinal response. Does R have a
command for conducting a multilevel ordinal logistic regression when the
model violates the parallel regression or proportional odds assumption?
Additionally, are there any tests to check the parallel regression
I have an example of multilevel analysis with 3 levels, but data are
non-normally distributed. In case of normal distribution, I would perform
multilevel linear analysis using lme function, but what should I do in case
of non-normal distribution?
thanks,
Srecko
[[alternative HTML version
On Sep 30, 2013, at 2:50 PM, srecko joksimovic wrote:
I have an example of multilevel analysis with 3 levels, but data are
non-normally distributed. In case of normal distribution, I would perform
multilevel linear analysis using lme function, but what should I do in case
of non-normal
I thought so, but then I found this:
Normality
The assumption of normality states that the error terms at every level of
the model are normally distributed
maybe I misinterpreted something.
On Mon, Sep 30, 2013 at 3:06 PM, David Winsemius dwinsem...@comcast.netwrote:
On Sep 30, 2013, at
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On Behalf Of srecko joksimovic
Sent: Monday, September 30, 2013 3:22 PM
To: David Winsemius
Cc: R help
Subject: Re: [R] multilevel analysis
I thought so, but then I found
On Sep 30, 2013, at 3:22 PM, srecko joksimovic wrote:
I thought so, but then I found this:
Normality
The assumption of normality states that the error terms at every level of the
model are normally distributed
maybe I misinterpreted something.
Notice that it is the _error_terms_ that are
Thanks for your comments, David and Bert.
The best would be to provide an example. Let's say we have a dataset like
this one:
IDEmployee Company OU CountViewPortal CountLogin TimeOnTask Performance
1 Company1 Company1.OU1 21 33 627.8 4.3
2 Company1 Company1.OU2 45 54 34.8 2.3
3 Company2
Hi,
i am trying to learn something about multilevel analysis using a great
Discovering statistics using R. I constructed some sample data and then
tried to fit a model. Generally model fits well, however when trying to fit
the same model using z-score (standarizded) variables i got an error:
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