[R] fixed effects following lmer and mcmcsamp - which to present?
Henrik Parn
henrik.parn at bio.ntnu.no
Tue Aug 8 12:11:40 CEST 2006
Dear all,
I am running a mixed model using lmer. In order to obtain CI of
individual coefficients I use mcmcsamp. However, I need advice which
values that are most appropriate to present in result section of a
paper. I have not used mixed models and lmer so much before so my
question is probably very naive. However, to avoid to much problems with
journal editors and referees addicted to p-values, I would appreciate
advice of which values of the output for the fixed factor that would be
most appropriate to present in a result section, in order to convince
them of the p-value free 'lmer-mcmcsamp'-approach!
Using the example from the help page on lmer:
fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
...I obtain the following for 'Days':
summary(fm1)
...
Estimate Std. Error t value
Days 10.4673 1.5458 6.771
...and from mcmcsamp:
summary(mcmcsamp(fm1 , n = 10000))
1. Empirical mean and standard deviation for each variable,
plus standard error of the mean:
Mean SD Naive SE Time-series SE
Days 10.4695 1.7354 0.017354 0.015921
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
Days 7.0227 9.3395 10.4712 11.5719 13.957
The standard way of presenting coefficients following a 'non-lmer'
output is often (beta=..., SE=..., statistic=..., P=...). What would be
the best equivalent in a 'lmer-mcmcsamp-context'? (beta=..., CI=...) is
a good start I believe. But which beta? And what else?
I assume that the a 95% CI in this case would be 7.0227-13.957 (please,
do correct me I have completely misunderstood!). But which would be the
corresponding beta? 10.4673?, 10.4695? 10.4712? Is the t-value worth
presenting or is it 'useless' without corresponding degrees of freedom
and P-value? If I present the mcmcsamp-CI, does it make sense to present
any of the three SE obtained in the output above? BTW, I have no idea
what Naive SE, Time-series SE means. Could not find much in help and
pdfs to coda or Matrix, or in Google.
Thanks in advance for any advice and hints to help-texts I have missed!
Best regards,
Henrik
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