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
www.inbo.be
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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
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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
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