Covariance structure used in lme

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 at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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Op vr 22 mei 2020 om 15:19 schreef Sorkin, John <jsorkin at 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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