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)
Covariance structure used in lme
3 messages · Sorkin, John, Thierry Onkelinx, Vaida, Florin
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 /////////////////////////////////////////////////////////////////////////////////////////// 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 /////////////////////////////////////////////////////////////////////////////////////////// <https://www.inbo.be> 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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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 at 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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