[R-sig-ME] model specification for repeated measure
haveaballphysio at gmail.com
Thu Feb 1 20:52:16 CET 2018
Thanks for the suggestions. I greatly appreciate you taking the time, and I
look forward to trying out the ideas.
On Fri, Feb 2, 2018 at 5:53 AM, Malcolm Fairbrother <
malcolm.fairbrother at umu.se> wrote:
> Hi Dot,
> This specification would yield a single coefficient for the
> between-individual and within-individual effects. That is, you’re assuming
> the association is the same over time as it is across individuals at a
> single point in time. I wouldn’t expect this to be a safe assumption, and
> there’s a pretty straightforward fix: centre your time-varying predictors
> by their mean for each person. That will yield within effects equivalent to
> what you’d get from a fixed effects model.
> For more information about this, see:
> Hope that’s useful,
> Malcolm Fairbrother
> Professor of Sociology
> Umeå University <http://www.umu.se/english>
> Date: Thu, 1 Feb 2018 21:07:10 +1030
> From: Dot Dumuid <haveaballphysio at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] model specification for repeated measure
> Dear mixed model experts,
> We have a dataset of older adults. We measured their mental health (MH) 6
> months before retirement and again 12 months post retirement.
> At both of these time points we also measured their physical activity (PA)
> (min/day), income (INC) and general health (GH).
> We would like to create a model that tells us if change in physical
> activity over the retirement threshold predicts change in mental health,
> and we'd like to use the model to predict how much mental health is
> predicted to change when physical activity is increased from perhaps 15
> minutes to 60 minutes. We'd like to use a mixed model rather than just
> using change (difference) scores. And we'd like to control for things like
> change in general physical health and change in income.
> This is what the data look like
> *ID time MH PA GH INC*
> 01 pre 4 15 56 560
> 02 pre 5 30 30 1200
> .. ..... .. .. .. ...
> 01 post 7 40 50 50
> 02 post 8 45 30 0
> I'm not sure how best to build the model. Something like this?
> model <- lmer (MH ~ PA * time + GH + INC + (1|participant.ID) )
> Thank you in advance.
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