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List:       r-sig-mixed-models
Subject:    Re: [R-sig-ME] Covariance structure used in lme
From:       "Vaida, Florin" <fvaida () health ! ucsd ! edu>
Date:       2020-05-22 16:00:29
Message-ID: 3DDE2546-6085-4176-B511-971D2A270991 () health ! ucsd ! edu
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John, this is indeed an unstructured variance-covariance matrix for the random \
effects. Not to be confused with the covariance matrix of the longitudinal vector of \
observations, which in the context of the general linear model can also be modeled in \
a variety of ways, including unstructured (gls() function in R).

Florin

> On May 22, 2020, at 6:19 AM, Sorkin, John <jsorkin@som.umaryland.edu> wrote:
> 
> 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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> 
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