[R-sig-ME] Covariance structure used in lme
|v@|d@ @end|ng |rom he@|th@uc@d@edu
Fri May 22 18:00:29 CEST 2020
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).
> On May 22, 2020, at 6:19 AM, Sorkin, John <jsorkin using 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)
> 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 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
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