[R-sig-ME] covariance matrix of fixed effects in lmer()

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Fri Aug 10 14:29:56 CEST 2012

Dear Matthias,

vcov(model) for the fixed effects and VarCorr(model) for the random effects

Best regards

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx op inbo.be

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces op r-project.org [mailto:r-sig-mixed-models-bounces op r-project.org] Namens matthias.suter op art.admin.ch
Verzonden: vrijdag 10 augustus 2012 13:27
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] covariance matrix of fixed effects in lmer()

Dear list members

Is there a direct access to the covariance matrix of fixed effects in lmer(). For example, with ...

summary( lmer(y ~ x1 + x2 + x3  + (1 | Groupingfactor), data) )

... we receive the    "Correlation of Fixed Effects"

Is there a straight forward way to get the covariance matrix to which the correlation matrix refers?

The analogue in a lm() would be:

lmout <- lm(y ~ x1 + x2 + x3, data)

To get the covariance matrix, we have e.g.:

summary(lmout)$sigma^2 * summary(lmout)$cov.uns

With many thanks in advance,

Matthias Suter, Dr.
Forage and Grassland
Forschungsanstalt Agroscope Reckenholz-Tänikon ART Reckenholzstrasse 191, CH-8046 Zürich

Tel. +41 44 377 75 90
Fax +41 44 377 72 01
matthias.suter op art.admin.ch

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