[R-sig-ME] model specification for repeated measure
haveaballphysio at gmail.com
Thu Feb 1 15:49:58 CET 2018
On Thu, Feb 1, 2018 at 10:54 PM, Thierry Onkelinx <thierry.onkelinx at inbo.be>
> Dear Dot,
> The specification of your covariates seems reasonable to me. You need
> to check if the Gaussian distribution is relevant for your
> measurements on mental health.
> 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
> 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
> 2018-02-01 11:37 GMT+01:00 Dot Dumuid <haveaballphysio at gmail.com>:
> > 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
> > (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
> > 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.
> > Dot
> > [[alternative HTML version deleted]]
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