Master's Partial Credit with lmer [was:RE: lmer]
I approached this problem by creating a dichotomous item for each threshold of the rated item. For a 4-level rating (0,1,2,3,4), I created 3 dichotomous items. A score of 0 translates to 0,0,0; 1=1,0,0; 2=1,1,0 and 3=1,1,1. Then I combined my created items with the originally dichotomous items and ran models using lmer as described in the paper. I don't know if the mathematics works out exactly, but I think this is logically equivalent to the Partial Credit Model. If someone see a theoretical reason not to do this, I'd be interested to know. Clearly, this tactic weights the data from the rated item more than a dichotomous items (3x in my example), but that was appropriate for my purposes.
Iasonas Lamprianou wrote:
Thank you Doran for your response. If anyone else is aware of any other R package that can run multilevel Rasch/IRT models, please respond. Jason 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 Sat, 6/12/08, Doran, Harold <HDoran at air.org> wrote:
From: Doran, Harold <HDoran at air.org> Subject: Master's Partial Credit with lmer [was:RE: [R-sig-ME] lmer] To: lamprianou at yahoo.com, r-sig-mixed-models at r-project.org Date: Saturday, 6 December, 2008, 3:34 PM The answer is no, the PCM cannot be run using lmer. Also, it is best not to ask a new question by replying to a different thread. Harold -----Original Message----- From: r-sig-mixed-models-bounces at r-project.org on behalf of Iasonas Lamprianou Sent: Fri 12/5/2008 5:10 PM To: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] lmer 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 Send R-sig-mixed-models mailing list submissions to r-sig-mixed-models at r-project.org To subscribe or unsubscribe via the World Wide Web,
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https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models or, via email, send a message with subject or body 'help' to r-sig-mixed-models-request at r-project.org You can reach the person managing the list at r-sig-mixed-models-owner at r-project.org When replying, please edit your Subject line so it is more specific than "Re: Contents of R-sig-mixed-models digest..." Today's Topics: 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 ------------------------------ _______________________________________________ R-sig-mixed-models mailing list R-sig-mixed-models at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models End of R-sig-mixed-models Digest, Vol 24, Issue 4 ************************************************* _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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