Hello David, thank you for the links.

I found out this code was packed into a package:
http://cran.r-project.org/web/packages/nparLD/index.html
It appears this doesn't offer a general solution, but it does allow for a
more complex solution then what I have found by now.

It also seems there is a place in building a wrapper to the functions in the
packages so that they would be able to work with a function structure.

If anyone comes up with other packages/code, that you think are relevant,
I'd be happy to know about it.

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 Fri, Jun 18, 2010 at 6:54 PM, David Winsemius <dwinsemius@comcast.net>wrote:

>
> On Jun 18, 2010, at 5:00 AM, Maciej Hoffman-Wecker wrote:
>
>
>> 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, München.
>>
>
> The author does not provide an English page for his R programs, but the R
> code for his non-parametric longitudinal analysis approach appears
> documented in English:
>
> http://www.ams.med.uni-goettingen.de/amsneu/sasmakr-de.html
> http://www.ams.med.uni-goettingen.de/amsneu/download/longit/ld.f1.r
>
> http://www.ams.med.uni-goettingen.de/amsneu/download/longit/readme_ldf_f2.pdf
> http://www.ams.med.uni-goettingen.de/amsneu/download/longit/ld.f2.r
> http://www.ams.med.uni-goettingen.de/amsneu/download/longit/f1.ld.f1.r
>
>
>
>> 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@r-project.org/msg87977.html
>>
>
> (Which demonstrates among other things the lability of my long-term memory,
> as did Frank's note about the deficiencies of the rank transform approach,
> which I am reasonably sure he has also made to me before. Thanks to both of
> you for the reminder.)
> --
> David.
>
>
>
>> Regards,
>> Maciej
>>
>> -----Ursprüngliche Nachricht-----
>>
>>
>> Message: 117
>> Date: Thu, 17 Jun 2010 20:12:54 -0400
>> From: David Winsemius <dwinsemius@comcast.net>
>> To: Tal Galili <tal.galili@gmail.com>
>> Cc: r-help@r-project.org
>> Subject: Re: [R] Is there a non-parametric repeated-measures Anova in
>>        R ?
>> Message-ID: <B48AA7EE-0FC2-4CDF-B021-6904123050EF@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
>>>
>>>
>
>>>
>>> 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
>>>>>
>>>> --
>>>> Jeremy Miles
>>>> Psychology Research Methods Wiki: www.researchmethodsinpsychology.com
>>>>
>>>>
>> David Winsemius, MD
>> West Hartford, CT
>>
>> ------------------------------
>>
>> Message: 118
>> Date: Thu, 17 Jun 2010 18:32:33 -0700
>> From: "Rex C. Eastbourne" <rex.eastbourne@gmail.com>
>> To: r-help@r-project.org
>> Subject: [R] Drawing paths through a grid
>> Message-ID:
>>        <AANLkTimbItr_efpYalgJxPGBJL2sSWj6YfenACv89IzL@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@illinois.edu>
>> To: r-help@r-project.org
>> Subject: [R] is there a function to find the quantile of the mean of a
>>        vector?
>> Message-ID:
>>        <AANLkTikFOYCypEsExyvN2kwXH1wUhjaXc9b_Y2Q8mSUg@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@gmail.com>
>> To: David LeBauer <dlebauer@illinois.edu>
>> Cc: r-help@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@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
>>>
>> ------------------------------
>>
>>
>> Message: 121
>> Date: Fri, 18 Jun 2010 00:03:18 -0400
>> From: David Winsemius <dwinsemius@comcast.net>
>> To: weller <weller.emmons@gmail.com>
>> Cc: r-help@r-project.org
>> Subject: Re: [R] Multiple ecdf plots?
>> Message-ID: <A5D54844-67B3-43E1-B1C6-DC437D248FEA@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
>>>
>>
>>
>> ------------------------------
>>
>> Message: 122
>> Date: Thu, 17 Jun 2010 23:09:00 -0500
>> From: Frank E Harrell Jr <f.harrell@Vanderbilt.Edu>
>> To: David Winsemius <dwinsemius@comcast.net>
>> Cc: r-help@r-project.org
>> Subject: Re: [R] Is there a non-parametric repeated-measures Anova in
>>        R ?
>> Message-ID: <4C1AF15C.40606@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@otago.ac.nz>
>> To: <deschamps.aline@yahoo.fr>
>> Cc: R Help <r-help@r-project.org>
>> Subject: Re: [R] Design of experiments for Choice-Based Conjoint
>>        Analysis (CBC)
>> Message-ID: <4C1AEFDB.7070002@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 ... ;-)
>> -------------- next part --------------
>> A non-text attachment was scrubbed...
>> Name: DCE with R.pdf
>> Type: application/pdf
>> Size: 934195 bytes
>> Desc: not available
>> URL: <
>> https://stat.ethz.ch/pipermail/r-help/attachments/20100618/a8d2f366/attachment.pdf
>> >
>> -------------- next part --------------
>> An embedded and charset-unspecified text was scrubbed...
>> Name: MktRoll.R
>> URL: <
>> https://stat.ethz.ch/pipermail/r-help/attachments/20100618/a8d2f366/attachment.pl
>> >
>>
>> ------------------------------
>>
>
> David Winsemius, MD
> West Hartford, CT
>
> ______________________________________________
> 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.
>

	[[alternative HTML version deleted]]

