[R-sig-ME] prediction intervals from a mixed-effects models?

[Reinhold Kliegl]

Spencer,

I think the Gelman & Hill (2007) book has examples that look less
complicated to me in comparison to what you describe (i.e. simply
sample from the estimated distributions). I have some code for
computation of power, also following examples in this book. Perhaps, I
am overlooking something.

Reinhold

On Sun, Apr 13, 2008 at 7:10 PM, Spencer Graves <spencer.graves at pdf.com> wrote:
>       How can I get prediction intervals from a mixed-effects model?
>  Consider the following example:
>
>  library(nlme)
>  fm3 <- lme(distance ~ age*Sex, data = Orthodont, random = ~ 1)
>  df3.1 <- with(Orthodont, data.frame(age=seq(5, 20, 5),
>                     Subject=rep(Subject[1], 4),
>                     Sex=rep(Sex[1], 4)))
>  predict(fm3, df3.1, interval='prediction')
>  #      M01      M01      M01      M01
>  # 22.69012 26.61199 30.53387 34.45574
>
>  # NOTE:  The 'interval' argument to the 'predict' function was ignored.
>  # It works works for an 'lm' object, but not an 'lme' object.
>
>       One way to do this might be via mcmcsamp of the corresponding
>  'lmer' model:
>
>  library(lme4)
>  set.seed(3)
>  samp3r <- mcmcsamp(fm3r, n=10000)
>  samp3r[1:2,]
>
>       Then use library(coda) to check convergence and write a function
>  to simulate a single observation from each set of simulated parameters
>  and compute quantile(..., c(.025, .975)) for each prediction level
>  desired.
>
>       However, before I coded that, I thought I would ask if some easier
>  method might be available.
>
>       Thanks,
>       Spencer
>  p.s.  RSiteSearch("lme prediction intervals") produced 3 hits including
>  2 from James A Rogers over 3 years ago.  In one, he said, "I am not
>  aware of any published R function that gives you prediction intervals or
>  tolerance intervals for lme models."
>  (http://finzi.psych.upenn.edu/R/Rhelp02a/archive/42781.html)   In the
>  other, he provided sample code for prediction or tolerance intervals of
>  lme variance components.
>  (http://finzi.psych.upenn.edu/R/Rhelp02a/archive/44675.html)
>
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