[R-sig-ME] lmer

[Iasonas Lamprianou]

Dear friends, 
does anyone know how (if) I can run a multilevel Partial Credit Rasch model using lmer? I am aware of the "Estimating the Multilevel Rasch Model: With the lme4 Package" but I think that this only refers to the dichotomous Rasch case. Or, alternatively, redirect me to any other free package that can handle multilevel Rasch/IRT models. 
Thanks

Dr. Iasonas Lamprianou
Department of Education
The University of Manchester
Oxford Road, Manchester M13 9PL, UK
Tel. 0044  161 275 3485
iasonas.lamprianou at manchester.ac.uk


--- On Thu, 4/12/08, r-sig-mixed-models-request at r-project.org <r-sig-mixed-models-request at r-project.org> wrote:

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> Subject: R-sig-mixed-models Digest, Vol 24, Issue 4
> To: r-sig-mixed-models at r-project.org
> Date: Thursday, 4 December, 2008, 11:00 AM
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> Today's Topics:
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>    1. Logisting regression for same-different speaker
>       classification (Leonardo LANCIA)
> 
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Wed, 3 Dec 2008 12:03:02 +0100 (CET)
> From: Leonardo LANCIA
> <Leonardo.Lancia at univ-provence.fr>
> Subject: [R-sig-ME] Logisting regression for same-different
> speaker
> 	classification
> To: r-sig-mixed-models at r-project.org
> Message-ID:
> <9555723.429.1228302182606.JavaMail.root at frontal1>
> Content-Type: text/plain; charset=iso-8859-1
> 
> Dear List,
> 
> I would like to use a mixed logistic regression model as a
> classifier which decides if two speech signals representing
> two istances of the same phoneme (uttered in a specified
> phentic context) are produced by the same speaker or not. To
> do that I should use a huge number of predictors (more or
> less 50 acoustic features). More over, for each acoustic
> feature I should specify a random interaction with the
> following factors : the phonetic label attached to the
> acoustic signals, and a phonetic label correspoding to the
> context from which the acoustic signals are extracted. 
> I am not interested in hypotesis testing but I would like
> to have an estimation of the contributoin to this task of
> each of the predictors and an estimate of the correction
> coefficients associated to the random effects.
> Do you think that a mixed logistic regression would do the
> job or should I move to Support vector machines algorithms?
> 
> Leonardo Lancia
> 
> 
> 
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