[R-sig-ME] Covariance structure used in lme

[Thierry Onkelinx]

Dear John,

Yes. Some people call a positive-definite matrix unstructured.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
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Op vr 22 mei 2020 om 15:19 schreef Sorkin, John <jsorkin using som.umaryland.edu>:

> I am running the following random slope, random intercept model:
> #  Model 3
> fitRSlope1 <- lme(distance~age+Sex+age*Sex,
> random=~1+age|Subject,data=Orthodont)
> summary(fitRSlope1)
> ranef(fitRSlope1)
>
> When I run the model, I get the following output
>
> Random effects:
>  Formula: ~1 + age | Subject
>  Structure: General positive-definite, Log-Cholesky parametrization
>             StdDev    Corr
> (Intercept) 2.4055009 (Intr)
> age         0.1803455 -0.668
> Residual    1.3100396
>
> Is the general positive-definite, Log-Cholesky parametrization a
> description of what one might call an unstructured variance-covariance
> matrix with as a particular paramaterization?
>
> Thank  you,
> John
>
>
>
>
>
> John David Sorkin M.D., Ph.D.
> Professor of Medicine
> Chief, Biostatistics and Informatics
> University of Maryland School of Medicine Division of Gerontology and
> Geriatric Medicine
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> (Phone) 410-605-7119
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>
>
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