[R-sig-ME] R-sig-mixed-models Digest, Vol 138, Issue 34

[Trevor Walker]

Try googling "Cumulative link mixed model".

I have had success with the clm and clmm functions in the ordinal package.

-Trevor

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1. mixed-effects ordinal logistic regression model
2. Re: mixed-effects ordinal logistic regression model
(Paul Buerkner)
----------------------------------------------------------------------
Message: 1
Date: Sat, 23 Jun 2018 23:04:44 +1000
Subject: [R-sig-ME] mixed-effects ordinal logistic regression model
Content-Type: text/plain; charset="utf-8"
Hi list
I have a dataset with n=60 animals with two groups (30/group; control and
treatment) on 3 different research sites. Animals are monitored on days 0,
14 and 28 (repeated measures), and lesions are scored from 1-4.
I want to use a mixed-effects ordinal logistic regression model and
consider
animals and research sites as random-effects in the model.
I haven't done ordinal logistic regression before, and I would like to use
this data and learn how to do the analysis and also interpret the outputs.
I
appreciate any help on;
1- A book, paper or link on ordinal logistic regression (easy to read and
understand for an average reader)
2- What is the preferred package in R to analyse such data? I noticed some
have used "Ordinal" package.
3- Is it appropriate to use farm (n=3) as a random-effects in the model? I
assume 3 is small to be considered as a random-effects in the model, your
thoughts?
4- Because observations are repeated on 3 occasions (repeated measures), I
intend to use animals as a random-effects.
5- If I use both research sites and animals as random-effects, I assume it
would be a nested random-effects model?
6- I appreciate if someone can help with some R codes on ordinal logistic
regression
Your help is greatly appreciated!
Ahmad
[[alternative HTML version deleted]]
------------------------------
Message: 2
Date: Sat, 23 Jun 2018 15:30:41 +0200
Subject: Re: [R-sig-ME] mixed-effects ordinal logistic regression
model
mail.gmail.com>
Content-Type: text/plain; charset="utf-8"
Hi Ahmad,
if you want to fit this model in a frequentist framework, I recommend the
"ordinal" package. If you rather want to use a Bayesian framework, I
recommend "brms". For a tutorial paper about ordinal models also containing
R code for brms, see https://psyarxiv.com/x8swp/
Paul

Hi list
I have a dataset with n=60 animals with two groups (30/group; control and
treatment) on 3 different research sites. Animals are monitored on days

0,

14 and 28 (repeated measures), and lesions are scored from 1-4.
I want to use a mixed-effects ordinal logistic regression model and
consider
animals and research sites as random-effects in the model.
I haven't done ordinal logistic regression before, and I would like to

use

this data and learn how to do the analysis and also interpret the

outputs.

I
appreciate any help on;
1- A book, paper or link on ordinal logistic regression (easy to read and
understand for an average reader)
2- What is the preferred package in R to analyse such data? I noticed

some

have used "Ordinal" package.
3- Is it appropriate to use farm (n=3) as a random-effects in the model?

I

assume 3 is small to be considered as a random-effects in the model, your
thoughts?
4- Because observations are repeated on 3 occasions (repeated measures),

I

intend to use animals as a random-effects.
5- If I use both research sites and animals as random-effects, I assume

it

would be a nested random-effects model?
6- I appreciate if someone can help with some R codes on ordinal logistic
regression
Your help is greatly appreciated!
Ahmad
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https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

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[a***@tpg.com.au]

Hi Paul/Trever

Thanks for these, much appreciated!
I will try these to see which one works, and how the outputs can be
interpreted.


Ahmad



-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-***@r-project.org> On
Behalf Of Trevor Walker
Sent: Monday, 25 June 2018 11:53 AM
To: r-sig-mixed-***@r-project.org
Subject: Re: [R-sig-ME] R-sig-mixed-models Digest, Vol 138, Issue 34

Try googling "Cumulative link mixed model".

I have had success with the clm and clmm functions in the ordinal package.

-Trevor

Send R-sig-mixed-models mailing list submissions to
To subscribe or unsubscribe via the World Wide Web, visit
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
or, via email, send a message with subject or body 'help' to
You can reach the person managing the list at
When replying, please edit your Subject line so it is more specific
than "Re: Contents of R-sig-mixed-models digest..."
1. mixed-effects ordinal logistic regression model
2. Re: mixed-effects ordinal logistic regression model
(Paul Buerkner)
----------------------------------------------------------------------
Message: 1
Date: Sat, 23 Jun 2018 23:04:44 +1000
Subject: [R-sig-ME] mixed-effects ordinal logistic regression model
Content-Type: text/plain; charset="utf-8"
Hi list
I have a dataset with n=60 animals with two groups (30/group; control and
treatment) on 3 different research sites. Animals are monitored on days 0,
14 and 28 (repeated measures), and lesions are scored from 1-4.
I want to use a mixed-effects ordinal logistic regression model and
consider animals and research sites as random-effects in the model.
I haven't done ordinal logistic regression before, and I would like to
use this data and learn how to do the analysis and also interpret the

outputs.

I
appreciate any help on;
1- A book, paper or link on ordinal logistic regression (easy to read
and understand for an average reader)
2- What is the preferred package in R to analyse such data? I noticed
some have used "Ordinal" package.
3- Is it appropriate to use farm (n=3) as a random-effects in the
model? I assume 3 is small to be considered as a random-effects in the
model, your thoughts?
4- Because observations are repeated on 3 occasions (repeated
measures), I intend to use animals as a random-effects.
5- If I use both research sites and animals as random-effects, I
assume it would be a nested random-effects model?
6- I appreciate if someone can help with some R codes on ordinal
logistic regression
Your help is greatly appreciated!
Ahmad
[[alternative HTML version deleted]]
------------------------------
Message: 2
Date: Sat, 23 Jun 2018 15:30:41 +0200
Subject: Re: [R-sig-ME] mixed-effects ordinal logistic regression
model
mail.gmail.com>
Content-Type: text/plain; charset="utf-8"
Hi Ahmad,
if you want to fit this model in a frequentist framework, I recommend
the "ordinal" package. If you rather want to use a Bayesian framework,
I recommend "brms". For a tutorial paper about ordinal models also
containing R code for brms, see https://psyarxiv.com/x8swp/
Paul

Hi list
I have a dataset with n=60 animals with two groups (30/group; control and
treatment) on 3 different research sites. Animals are monitored on days

0,

14 and 28 (repeated measures), and lesions are scored from 1-4.
I want to use a mixed-effects ordinal logistic regression model and
consider animals and research sites as random-effects in the model.
I haven't done ordinal logistic regression before, and I would like to

use

this data and learn how to do the analysis and also interpret the

outputs.

I
appreciate any help on;
1- A book, paper or link on ordinal logistic regression (easy to
read and understand for an average reader)
2- What is the preferred package in R to analyse such data? I noticed

some

have used "Ordinal" package.
3- Is it appropriate to use farm (n=3) as a random-effects in the model?

I

assume 3 is small to be considered as a random-effects in the model,
your thoughts?
4- Because observations are repeated on 3 occasions (repeated measures),

I

intend to use animals as a random-effects.
5- If I use both research sites and animals as random-effects, I assume

it

would be a nested random-effects model?
6- I appreciate if someone can help with some R codes on ordinal
logistic regression
Your help is greatly appreciated!
Ahmad
[[alternative HTML version deleted]]
_______________________________________________
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

[[alternative HTML version deleted]]
------------------------------
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