R help - Jun 2010 - Is there a non-parametric repeated-measures Anova in R ?

Hello Prof. Harrell and dear R-help mailing list,

I wish to perform a non-parametric repeated measures anova.

If what I read online is true, this could be achieved using a mixed Ordinal
Regression model (a.k.a: Proportional Odds Model).
I found two packages that seems relevant, but couldn't find any vignette on
the subject:
http://cran.r-project.org/web/packages/repolr/
http://cran.r-project.org/web/packages/ordinal/

So being new to the subject matter, I was hoping for some directions from
people here.

Are there any tutorials/suggested-reading on the subject?  Even better, can
someone suggest a simple example code for how to run and analyse this in R
(e.g: "non-parametric repeated measures anova") ?

I waited a week to repost this question.  If I should have waited longer, or
not repost this at all - then I am truly sorry.

Thanks for any help,
Tal




>
> ----------------Contact
> Details:-------------------------------------------------------
> Contact me: Tal.Galili@gmail.com |  972-52-7275845
> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
> www.r-statistics.com (English)
>
>
----------------------------------------------------------------------------------------------
>
>
>
	[[alternative HTML version deleted]]

It's possible to use the ordinal regression model if your data are
ordered categories.  The standard non-parametric test is the Friedman
test.

?friedman.test

Jeremy

On 16 June 2010 10:22, Tal Galili <tal.galili at gmail.com>
wrote:
> Hello Prof. Harrell and dear R-help mailing list,
>
> I wish to perform a non-parametric repeated measures anova.
>
> If what I read online is true, this could be achieved using a mixed Ordinal
> Regression model (a.k.a: Proportional Odds Model).
> I found two packages that seems relevant, but couldn't find any
vignette on
> the subject:
> http://cran.r-project.org/web/packages/repolr/
> http://cran.r-project.org/web/packages/ordinal/
>
> So being new to the subject matter, I was hoping for some directions from
> people here.
>
> Are there any tutorials/suggested-reading on the subject?  Even better, can
> someone suggest a simple example code for how to run and analyse this in R
> (e.g: "non-parametric repeated measures anova") ?
>
> I waited a week to repost this question.  If I should have waited longer,
or
> not repost this at all - then I am truly sorry.
>
> Thanks for any help,
> Tal
>

Hello Jeremy,
Thank you for replying.

I came across friedman test (I even wrote and published R code to easily
perform a post-hoc analysis of friedman
test<http://www.r-statistics.com/2010/02/post-hoc-analysis-for-friedmans-test-r-code/>
).
But what I am after is *multi-way* repeated-measures anova.  Thank you for
your reply which allowed me to clarify my intentions.

Best,
Tal




----------------Contact
Details:-------------------------------------------------------
Contact me: Tal.Galili@gmail.com |  972-52-7275845
Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
www.r-statistics.com (English)
----------------------------------------------------------------------------------------------




On Wed, Jun 16, 2010 at 8:30 PM, Jeremy Miles
<jeremy.miles@gmail.com>wrote:
> It's possible to use the ordinal regression model if your data are
> ordered categories.  The standard non-parametric test is the Friedman
> test.
>
> ?friedman.test
>
> Jeremy
>
>
> On 16 June 2010 10:22, Tal Galili <tal.galili@gmail.com> wrote:
> > Hello Prof. Harrell and dear R-help mailing list,
> >
> > I wish to perform a non-parametric repeated measures anova.
> >
> > If what I read online is true, this could be achieved using a mixed
> Ordinal
> > Regression model (a.k.a: Proportional Odds Model).
> > I found two packages that seems relevant, but couldn't find any
vignette
> on
> > the subject:
> > http://cran.r-project.org/web/packages/repolr/
> > http://cran.r-project.org/web/packages/ordinal/
> >
> > So being new to the subject matter, I was hoping for some directions
from
> > people here.
> >
> > Are there any tutorials/suggested-reading on the subject?  Even
better,
> can
> > someone suggest a simple example code for how to run and analyse this
in
> R
> > (e.g: "non-parametric repeated measures anova") ?
> >
> > I waited a week to repost this question.  If I should have waited
longer,
> or
> > not repost this at all - then I am truly sorry.
> >
> > Thanks for any help,
> > Tal
> >
> >
> >
> >
> >
> >>
> >> ----------------Contact
> >> Details:-------------------------------------------------------
> >> Contact me: Tal.Galili@gmail.com |  972-52-7275845
> >> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il
(Hebrew)
> |
> >> www.r-statistics.com (English)
> >>
> >>
>
----------------------------------------------------------------------------------------------
> >>
> >>
> >>
> >
> >        [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@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.
> >
>
>
>
> --
> Jeremy Miles
> Psychology Research Methods Wiki: www.researchmethodsinpsychology.com
>
	[[alternative HTML version deleted]]

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Tal Galili
> Sent: Wednesday, June 16, 2010 10:44 AM
> To: Jeremy Miles
> Cc: r-help at r-project.org
> Subject: Re: [R] Is there a non-parametric repeated-measures Anova in R
> ?
> 
> Hello Jeremy,
> Thank you for replying.
> 
> I came across friedman test (I even wrote and published R code to
> easily
> perform a post-hoc analysis of friedman
> test<http://www.r-statistics.com/2010/02/post-hoc-analysis-for-
> friedmans-test-r-code/>
> ).
> But what I am after is *multi-way* repeated-measures anova.  Thank you
> for
> your reply which allowed me to clarify my intentions.
> 
> Best,
> Tal
> 
> 
> 
Tal,

Maybe this paper will give you some ideas. 

     http://www.apsnet.org/phyto/janpdf/1113-01O.pdf

Hope this is helpful,

Dan

Daniel J. Nordlund
Washington State Department of Social and Health Services
Planning, Performance, and Accountability
Research and Data Analysis Division
Olympia, WA 98504-5204

I always wonder why no one of the real experts posts a reference to E. Brunners
work:

Brunner, E., Domhof S. and Langer, F. (2002). Nonparametric Analysis of
Longitudinal Data in Factorial Designs. Wiley, New York.

The german book is great:

Brunner, E. und Langer, F. (1999). Nichtparametrische Analyse longitudinaler
Daten. Oldenbourg, M?nchen.

As far as I can remember it covers factorial designs as well. 

There is even r code, but you have to google for.

I googled by myself and found this post:

http://www.mail-archive.com/r-help at r-project.org/msg87977.html

Regards,
Maciej

-----Urspr?ngliche Nachricht-----


Message: 117
Date: Thu, 17 Jun 2010 20:12:54 -0400
From: David Winsemius <dwinsemius at comcast.net>
To: Tal Galili <tal.galili at gmail.com>
Cc: r-help at r-project.org
Subject: Re: [R] Is there a non-parametric repeated-measures Anova in
	R ?
Message-ID: <B48AA7EE-0FC2-4CDF-B021-6904123050EF at comcast.net>
Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes


On Jun 16, 2010, at 1:43 PM, Tal Galili wrote:
> Hello Jeremy,
> Thank you for replying.
>
> I came across friedman test (I even wrote and published R code to  
> easily
> perform a post-hoc analysis of friedman
>
test<http://www.r-statistics.com/2010/02/post-hoc-analysis-for-friedmans-test-r-code/
> >
> ).
> But what I am after is *multi-way* repeated-measures anova.  Thank  
> you for
> your reply which allowed me to clarify my intentions.
Many years ago I remember reading advice in Conover and Iman's  
"Practical Non-Parametric Statistics" that one could apply a rank  
transformation to the dependent  and independent variables and then  
run a typical anova test. This is probably inferior in many ways to  
doing quantile regression (don't know if this has a repeated measures  
extension) or to the use of robust standard errors for examining  
inferential issues in regression models, but it certainly represents a  
useful consistency check when all you are worried about is influential  
points in a  skew distributions. I cannot comment on how it would  
theoretically behave in a repeated-measures analysis, but I suspect  
that there are readers of this list who can comment with greater  
authority, and I invite them to do so.

-- 
David.
>
> Best,
> Tal
>
>
>
>
> ----------------Contact
> Details:-------------------------------------------------------
> Contact me: Tal.Galili at gmail.com |  972-52-7275845
> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il  
> (Hebrew) |
> www.r-statistics.com (English)
>
----------------------------------------------------------------------------------------------
>
>
>
>
> On Wed, Jun 16, 2010 at 8:30 PM, Jeremy Miles  
> <jeremy.miles at gmail.com>wrote:
>
>> It's possible to use the ordinal regression model if your data are
>> ordered categories.  The standard non-parametric test is the Friedman
>> test.
>>
>> ?friedman.test
>>
>> Jeremy
>>
>>
>> On 16 June 2010 10:22, Tal Galili <tal.galili at gmail.com>
wrote:
>>> Hello Prof. Harrell and dear R-help mailing list,
>>>
>>> I wish to perform a non-parametric repeated measures anova.
>>>
>>> If what I read online is true, this could be achieved using a mixed
>> Ordinal
>>> Regression model (a.k.a: Proportional Odds Model).
>>> I found two packages that seems relevant, but couldn't find any
>>> vignette
>> on
>>> the subject:
>>> http://cran.r-project.org/web/packages/repolr/
>>> http://cran.r-project.org/web/packages/ordinal/
>>>
>>> So being new to the subject matter, I was hoping for some  
>>> directions from
>>> people here.
>>>
>>> Are there any tutorials/suggested-reading on the subject?  Even  
>>> better,
>> can
>>> someone suggest a simple example code for how to run and analyse  
>>> this in
>> R
>>> (e.g: "non-parametric repeated measures anova") ?
>>>
>>> I waited a week to repost this question.  If I should have waited  
>>> longer,
>> or
>>> not repost this at all - then I am truly sorry.
>>>
>>> Thanks for any help,
>>> Tal
>>>
>>>
>>>
>>>
>>>
>>>>
>>>> ----------------Contact
>>>> Details:-------------------------------------------------------
>>>> Contact me: Tal.Galili at gmail.com |  972-52-7275845
>>>> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il  
>>>> (Hebrew)
>> |
>>>> www.r-statistics.com (English)
>>>>
>>>>
>>
----------------------------------------------------------------------------------------------
>>>>
>>>>
>>>>
>>>
>>>       [[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.
>>>
>>
>>
>>
>> --
>> Jeremy Miles
>> Psychology Research Methods Wiki: www.researchmethodsinpsychology.com
>>
>
> 	[[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.
David Winsemius, MD
West Hartford, CT



------------------------------

Message: 118
Date: Thu, 17 Jun 2010 18:32:33 -0700
From: "Rex C. Eastbourne" <rex.eastbourne at gmail.com>
To: r-help at r-project.org
Subject: [R] Drawing paths through a grid
Message-ID:
	<AANLkTimbItr_efpYalgJxPGBJL2sSWj6YfenACv89IzL at mail.gmail.com>
Content-Type: text/plain; charset=ISO-8859-1

I would like to draw a set of points that are equally spaced in a 2-D
grid. Then I would like to draw lines that illustrate different
directed paths through subsets of points. Imagine that the points
correspond to booths in a conference center, and I want to show the
various paths people took to visit the booths (using color to
highlight different types of paths). An example path might be: [(1,1),
(1,3), (3, 3)].

Note: I would like to also make the size of the points in the grid
variable (they correspond to the sizes of the booth).

Can anyone suggest a way to do this in R? (Or to suggest another
software package.)

Thanks,
Rex



------------------------------

Message: 119
Date: Thu, 17 Jun 2010 18:06:05 -0500
From: David LeBauer <dlebauer at illinois.edu>
To: r-help at r-project.org
Subject: [R] is there a function to find the quantile of the mean of a
	vector?
Message-ID:
	<AANLkTikFOYCypEsExyvN2kwXH1wUhjaXc9b_Y2Q8mSUg at mail.gmail.com>
Content-Type: text/plain; charset=ISO-8859-1

Hello,

I am interested in finding the quantile of the mean of a vector,
something analogous to using the pnorm(), but for an mcmc chain
instead of a distribution with known parameters.

One approach would be to write a function that finds the index of x_i
that minimizes (x-mean(x))^2

I suspect there is a function available to do this, but I can't find it?

Thank you,

David



------------------------------

Message: 120
Date: Thu, 17 Jun 2010 23:15:31 -0400
From: Jorge Ivan Velez <jorgeivanvelez at gmail.com>
To: David LeBauer <dlebauer at illinois.edu>
Cc: r-help at r-project.org
Subject: Re: [R] is there a function to find the quantile of the mean
	of a	vector?
Message-ID:
	<AANLkTinNoj5zxzFpea_eb5ZwC--mgux31950Ou0Bi0La at mail.gmail.com>
Content-Type: text/plain

Hi David,

You might try:
> set.seed(1)
> x <- runif(10, 3, 7)
> x
 [1] 4.062035 4.488496 5.291413 6.632831 3.806728 6.593559 6.778701 5.643191
5.516456 3.247145
> (x-mean(x))^2
 [1] 1.308783661 0.514892188 0.007285983 2.035688832 1.958118177 1.925165288
2.473214156
 [8] 0.191087609 0.096348590 3.837329960
> which.min((x-mean(x))^2)
[1] 3
> x[which.min((x-mean(x))^2)]
[1] 5.291413
> which.min(scale(x, scale = FALSE)**2)
[1] 3

See ?which.min and ?scale for more information.

HTH,
Jorge


On Thu, Jun 17, 2010 at 7:06 PM, David LeBauer <> wrote:
> Hello,
>
> I am interested in finding the quantile of the mean of a vector,
> something analogous to using the pnorm(), but for an mcmc chain
> instead of a distribution with known parameters.
>
> One approach would be to write a function that finds the index of x_i
> that minimizes (x-mean(x))^2
>
> I suspect there is a function available to do this, but I can't find
it?
>
> Thank you,
>
> David
>
> ______________________________________________
> 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]]



------------------------------

Message: 121
Date: Fri, 18 Jun 2010 00:03:18 -0400
From: David Winsemius <dwinsemius at comcast.net>
To: weller <weller.emmons at gmail.com>
Cc: r-help at r-project.org
Subject: Re: [R] Multiple ecdf plots?
Message-ID: <A5D54844-67B3-43E1-B1C6-DC437D248FEA at comcast.net>
Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes


On Jun 17, 2010, at 4:46 PM, weller wrote:
>
> I have a csv file that has approximately 50k rows.  In the first  
> value of
> each row, a file name is listed, and there are 162 different file  
> names.  At
> the end of each row, there is a number value.  What I would like to  
> be able
> to do is for the 162 different files (or we could call them  
> categories), is
> compute the ecdf for the values within that category.  Then plot the  
> ecdf
> for each file on the same graph.  Essentially, it would look  
> something like
>
http://www-stat.stanford.edu/~jtaylo/courses/stats202/R/chap3_data_exploration/iris_ecdf.png
> ,
> but instead of the 3 lines, it would show 162.  They don't have to be
> different colors, and the number of records in each file category  
> changes.
> I was considering using a  matrix and adding to it via a loop, but  
> couldn't
> quite get it to work. This is what I have right now.
>
> thwop <- read.csv("real_unmod_estimated_pI.csv", header=TRUE)
> filelist <- levels(thwop$Source)
> rig=matrix(nrows=162)
>
> for (i in filelist)
> {
>   thug <- subset(thwop,  == i)
>   rig[i,length(ecdf(thug$Estimated.pI))]=ecdf(thug$Source)
> }
> rig
Perhaps:

plot(x=0, y=min(thwop$estimated.pI), xlim=c(min(thwop$estimated.pI),  
max(thwop$estimated.pI) ), ylim=c(0.0, 1.0), xlab="",
ylab="")

tapply(thwop$estimated.pI, thwop$Source, function(x) { par(new=TRUE) ; 
plot(ecdf(x), verticals=TRUE, xlim=c(min(thwop$estimated.pI), max(thwop 
$estimated.pI, xaxt=FALSE) )) })

(It is going to be a mess.)


>
> Any help would be appreciated
>
>
> -- 
> View this message in context:
http://r.789695.n4.nabble.com/Multiple-ecdf-plots-tp2259465p2259465.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> 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.
David Winsemius, MD
West Hartford, CT



------------------------------

Message: 122
Date: Thu, 17 Jun 2010 23:09:00 -0500
From: Frank E Harrell Jr <f.harrell at Vanderbilt.Edu>
To: David Winsemius <dwinsemius at comcast.net>
Cc: r-help at r-project.org
Subject: Re: [R] Is there a non-parametric repeated-measures Anova in
	R ?
Message-ID: <4C1AF15C.40606 at vanderbilt.edu>
Content-Type: text/plain; charset="ISO-8859-1"; format=flowed

On 06/17/2010 07:12 PM, David Winsemius wrote:
>
> On Jun 16, 2010, at 1:43 PM, Tal Galili wrote:
>
>> Hello Jeremy,
>> Thank you for replying.
>>
>> I came across friedman test (I even wrote and published R code to
easily
>> perform a post-hoc analysis of friedman
>>
test<http://www.r-statistics.com/2010/02/post-hoc-analysis-for-friedmans-test-r-code/>
>>
>> ).
>> But what I am after is *multi-way* repeated-measures anova. Thank you
for
>> your reply which allowed me to clarify my intentions.
>
> Many years ago I remember reading advice in Conover and Iman's
> "Practical Non-Parametric Statistics" that one could apply a rank
> transformation to the dependent and independent variables and then run a
> typical anova test. This is probably inferior in many ways to doing
> quantile regression (don't know if this has a repeated measures
> extension) or to the use of robust standard errors for examining
> inferential issues in regression models, but it certainly represents a
> useful consistency check when all you are worried about is influential
> points in a skew distributions. I cannot comment on how it would
> theoretically behave in a repeated-measures analysis, but I suspect that
> there are readers of this list who can comment with greater authority,
> and I invite them to do so.
>
David - the rank transform method doesn't handle interactions properly, 
among other problems.  The proportional odds model is the logical 
extension of the Wilcoxon-Kruskal-Wallis approach.  It relies only on 
the rank of Y and reduces to the regular nonparametric tests as special 
cases.

Frank
-- 
Frank E Harrell Jr   Professor and Chairman        School of Medicine
                      Department of Biostatistics   Vanderbilt University



------------------------------

Message: 123
Date: Fri, 18 Jun 2010 16:02:35 +1200
From: John Williams <john.williams at otago.ac.nz>
To: <deschamps.aline at yahoo.fr>
Cc: R Help <r-help at r-project.org>
Subject: Re: [R] Design of experiments for Choice-Based Conjoint
	Analysis (CBC)
Message-ID: <4C1AEFDB.7070002 at otago.ac.nz>
Content-Type: text/plain; charset="iso-8859-1";
Format="flowed"

Hi,

You might find the attached article useful.  I am facing the same 
problems that you appear to be facing, and I found this article to be a 
great help.

I've also attached a small script I wrote to replicate the analyses 
presented in the paper. I can't reproduce the second analysis though, 
and I can't figure out what's wrong with my code.

HTH,

John.

P.S.  Is anyone else lurking interested in Discrete Choice Experiments 
and/or MaxDiff (Best/Worst) scaling.  Perhaps we could form a wee SIG?

Cheers,

John

P.S.  While there is no single function to do CBC/DCE in R, it doesn't 
seem too hard.  The only step for which there is no single function is 
aggregating the design matrix and data.  I've named my script/function 
MktRoll after the SAS macro that does that same job  (among many other 
things, I'm sure).  It's just a quick hack, though!

While it seems to be relatively straightforward in R, I have yet to 
actually implement a design and gather and analyse the data though ... ;-)
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------------------------------

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End of R-help Digest, Vol 88, Issue 21