From: Henrik Parn <henrik.parn_at_bio.ntnu.no>

Date: Tue 08 Aug 2006 - 20:11:40 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue Aug 08 20:36:25 2006

Date: Tue 08 Aug 2006 - 20:11:40 EST

Using the example from the help page on lmer:

fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)

...I obtain the following for 'Days':

summary(mcmcsamp(fm1 , n = 10000))

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

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue Aug 08 20:36:25 2006

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