[R-sig-ME] R-sig-mixed-models Digest, Vol 8, Issue 13
Iasonas Lamprianou
lamprianou at yahoo.com
Fri Aug 24 11:55:10 CEST 2007
Dear friends, in the following example, I am testing teacher effect. So, the teacher is affecting the score of the pupils, and the items are also random effects. The ID is the id of the pupils. Year (pupils are either year6 ot year7) and age and gender may play some role on the test score.
Question 1: In the following example, I am not sure what the Correlation of Fixed Effects is. Could someone please explain it to me? How do I use these numbers?
Question 2: Also, what is the meaning of this statment? Estimated scale (compare to 1 ) 0.865952 . How do I interpet this?
Thanks
Generalized linear mixed model fit using Laplace
Formula: SCORE ~ YEAR + AGE + GENDER + (1 | ITEM) + (1 | ID) + (1 | TEACHER)
Data: data2
Family: binomial(logit link)
AIC BIC logLik deviance
4979 5027 -2482 4965
Random effects:
Groups Name Variance Std.Dev.
ID 0.508153 0.71285
TEACHER 0.039018 0.19753
ITEM 3.874608 1.96840
number of obs: 7043, groups: ID, 754; TEACHER, 29; ITEM, 10
Estimated scale (compare to 1 ) 0.865952
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.6334597 0.6457887 -2.5294 0.0114 *
YEAR[T.Year 7] 0.0495903 0.1262880 0.3927 0.6946
AGE 0.0004811 0.0009579 0.5023 0.6155
GENDER[T.girl] -0.0546018 0.0924347 -0.5907 0.5547
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) YEAR[7 AGE
YEAR[T.Yr7] -0.101
AGE -0.203 -0.095
GENDER[T.g] -0.063 -0.067 -0.008
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
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Sent: Wednesday, 22 August, 2007 1:00:17 PM
Subject: R-sig-mixed-models Digest, Vol 8, Issue 13
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Today's Topics:
1. Re: R-sig-mixed-models Digest, Vol 8, Issue 10
(Iasonas Lamprianou)
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Message: 1
Date: Tue, 21 Aug 2007 03:35:05 -0700 (PDT)
Subject: Re: [R-sig-ME] R-sig-mixed-models Digest, Vol 8, Issue 10
To: r-sig-mixed-models at r-project.org
Message-ID: <607384.7411.qm at web54103.mail.re2.yahoo.com>
Content-Type: text/plain; charset=iso-8859-1
Hi all, I have a question regarding the paper
Estimating the Multilevel Rasch Model: With the
lme4 Package
by Doran, Bates etc al.
I would be greateful if someone could answer these questions, I am still a beginner.
In page 10, they fitted a model with no intercept. (1) What might be the motivation for this? (1) How would the interpretation be different if they had included am intercept? (3) How may I derive the table of residuals (observed-predicted)? (4) How can I estimate/evaluate the fit of individual items, subjects or companies?
I know that I have many questions, but I really love lme4 and I would like to routinely use it in my research. I have a great education dataset which I would love to analyse. Any experts wishing a joint publication? My motivation is to learn as much as possible during the collaboration for this paper.
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
----- Original Message ----
From: "r-sig-mixed-models-request at r-project.org" <r-sig-mixed-models-request at r-project.org>
To: r-sig-mixed-models at r-project.org
Sent: Wednesday, 15 August, 2007 1:00:14 PM
Subject: R-sig-mixed-models Digest, Vol 8, Issue 10
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Today's Topics:
1. Re: lmer, intercepts and offsets (Daniel Farewell)
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Message: 1
Date: Tue, 14 Aug 2007 16:39:02 +0000 (GMT)
From: Daniel Farewell <farewelld at cf.ac.uk>
Subject: Re: [R-sig-ME] lmer, intercepts and offsets
To: r-sig-mixed-models at r-project.org
Message-ID: <523651.84987.qm at web27101.mail.ukl.yahoo.com>
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This is a follow-up to a thread from back in March ("lmer, intercepts and offsets"). I'm hoping (at least) to better understand how lmer works.
I'd like to "trick" lmer into thinking it has converged to certain parameter estimates. This is straightforward for variance components, making use of the 'start' parameter and using 'control' to set the number of the various kinds of iteration to zero.
My ultimate goal is to extract posterior second moments from a model "fit" where I have specified both the variance components and the fixed effects.
Obviously it is possible to dig inside the fitted model and manually alter the fixed effects, but this has no impact on the result of a call to ranef, presumably because the posterior means (and variances?) have already been calculated, and are sitting in the ranef slot of the fitted model.
My question is this: at what stage do the random effects get calculated? Simplifying greatly, at some stage lmer must calculate betahat(Omega) (the fixed effects) and ranef(Omega) (the estimated random effects) for the converged value of Omega. Does ranef(Omega) depend on the result of betahat(Omega)? If so, then presumably tinkering with the betahat(Omega) results at the appropriate point inside lmer would result in what I want. If not (that is, if the dependence on the fixed effects is indirect) what needs tinkering with?
Is there a good reason why lmer does not allow models with no fixed effects at all? With the right offset, this would be another way to achieve the same result.
[[replacing trailing spam]]
Daniel Farewell
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