[R] Multi-level (nested) correlation structures via geepack package

Phat Chau ph@t@ch@u @end|ng |rom m@||@utoronto@c@
Mon Jul 13 17:40:56 CEST 2020

Hi Duncan, 

Thank you for your message. All of those citations and suggestions are wonderful and highly relevant for GEEs. My concern is that I need multi-level/nested correlation structures, whereas those handle the specification of covariance matrices at the level of repeated observations within-subject. I think the paper attached contains the theory behind such nested correlation structures, but the coding for it is not as apparent.


On 2020-07-12, 11:06 PM, "dulcalma dulcalma" <dulcalma using bigpond.com> wrote:

    Your choice of package should partly depend on the type of dependent 
    variable or Y that you are going to be dealing with
    categorical/ordinal data may involve different packages than continuous 
    or binary data see multgee for one.
    The number of samples can also make a difference GEE with the "correct 
    model" should normally have no problems with numbers 30-40; 25 or less 
    would normally require corrections and a diffence package.
    The doi for multgee  paper is 10.1111/biom.12054 and Touloumis paper in 
    Journal of Statistical Software
    For longitudinal data there is the following doi:
    a search for gee in the list of available packages should show you the 
    As a check of the result do the statistics on another package. I 
    remember doing a simple gee with an example
    from a book using 4 different packages 2 of which gave poor or 
    unreasonable answers
    Duncan Mackay
    Department of Agronomy and Soil Science
    University of New England
    ------ Original Message ------
    From: "Phat Chau" <phat.chau using mail.utoronto.ca>
    To: "r-help using R-project.org" <r-help using R-project.org>; "sorenh using math.aau.dk" 
    <sorenh using math.aau.dk>
    Sent: Sunday, 12 Jul, 2020 At 11:52 PM
    Subject: Re: [R]  Multi-level (nested) correlation structures via 
    geepack package
    I have a multi-level, cohort dataset with three levels: repeat measures 
    of a response (level 1), that are collected from individual participants 
    (level 2) who are students within a school (level 3). I would like to do 
    a generalized estimating equation (GEE) analysis of this clustered data, 
    but to do so I need to specify ‘nested’ correlation structures (e.g. 
    exchangeable, compound symmetric, Toeplitz) to account for the 
    within-individual and within-cluster correlations.
    Here is a reference paper that describes a nested exchangeable 
    correlation structure and nested compound symmetry: 
    The geepack is available in R to do GEE analyses, but it seems to me 
    that it only allows the user to specify a correlation structure via the 
    geepack(…‘corstr = ‘) option which only accounts for the 
    within-individual correlations (that arise from repeated measures). 
    Would it be possible to specify the nested correlation structures that I 
    refer to here to also account for the within-cluster correlations using 
    this package?
    Thank you,
    	[[alternative HTML version deleted]]
    R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
    PLEASE do read the posting guide 
    and provide commented, minimal, self-contained, reproducible code.

More information about the R-help mailing list