[R-sig-ME] Longitudinal covariation parameter estimate does not match average association over time
thierry.onkelinx at inbo.be
Fri Apr 8 09:59:32 CEST 2016
It looks like N differs along among ID. And that ID's with high overal N
have a low overal P. Try to plot N minus the average N per ID against P. I
would expect that this graph will show a positive correlation.
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
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
2016-04-07 19:37 GMT+02:00 Matthew Boden <matthew.t.boden op gmail.com>:
> I am thoroughly perplexed and could greatly benefit from your feedback
> (thanks in advance!).
> I am examining the longitudinal covariation between two variables (N, P)
> measured each month for 26 months among 140 subjects. I am interested in
> determining the average relation between these two variables when
> accounting for dependencies due to repeated measures. Thus, I am interested
> in between-subject variation more so than within-subject variation. Yet,
> there exists considerable variation in both trajectories and intercepts for
> individual subjects.
> The issue is that the average association between N and P at each time
> point is negative (e.g., r = -.29). Yet, in most LMM models I run, the
> fixed effects estimate for P predicting N is positive.
> For example, including random effects for both intercept and slope (to
> account for within subject variation in each) using the following code
> yields a positive estimate for P. This is also true if I include only a
> random effect for the intercept or a random effect for the slope.
> long <- lmer (N ~ P + (1 + P | ID), data = lip)
> Fixed effect estimate (SE), Z
> Intercept = 46.9 (4.38), 10.68
> P = 2.99 (.57), 5.19
> The only way I obtain a negative estimate for P is when I include
> duration/time in the model and a random effect for duration/time, but
> exclude the random effect for P AND exclude the random intercept.
> long <- lmer (N ~ P + time + (1 + time | ID), data = lip)
> Fixed effect estimate (SE), Z
> Intercept = 73.99 (2.17), 34.09
> Time = .18 (.06), 2.84
> P = -1.01 (.28), -3.51
> Besides the fact that I'm not really interested in the structure of N over
> time, and thus seemingly do not need a duration/time parameter, there is
> substantial variation in the intercept and slope for N by P for which
> random effects would be needed.
> It is my understanding, perhaps wrong, that the fixed effect parameter
> estimate for P should be akin to the average association between N and P
> across time. Thus, this parameter estimate should be negative, regardless
> of whether or not duration/time is included in the model. Indeed, plotting
> N by P without consideration of duration/time reveals a negative average
> regression slope.
> I'm at a loss and could use some help.
> Thank you,
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