Dear Paul,

It's certainly an issue that's coming up in various research domains with
which I'm familiar. I agree that the absence of any way of allowing for
autocorrelation (temporal AND spatial) is becoming an important limitation.

I looked at the issue a bit in a paper that came out in Political Science
Research and Methods earlier this year, but only by using simulations to
see how much of an effect longitudinally autocorrelated errors might have
in one context.

Hacking lme4 (or MCMCglmm -- see
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q3/016684.html) to
include something like corStruct is well beyond my abilities, but I would
be very happy to see you (or someone else) do it!

Cheers,
Malcolm



> Date: Tue, 5 Aug 2014 18:23:38 -0500
> From: Paul Johnson <pauljohn32@gmail.com>
> To: "R-SIG-Mixed-Models@r-project.org"
>         <r-sig-mixed-models@r-project.org>
> Subject: [R-sig-ME] autocorrelated errors
>
> Sorry to bother you, but
>
> I ask here every year or so if anybody is working on lme4 with time or
> spatially correlated random errors. Bring corStruct back to life?
>
> If nobody is, maybe we could start a project together? I'm getting
> great & fast estimates from lme4 with the cross sectional models, but
> to work on longitudinal panels, we need some AR(1) error terms, or
> such.
>
> pj
> --
> Paul E. Johnson
> Professor, Political Science      Assoc. Director
> 1541 Lilac Lane, Room 504      Center for Research Methods
> University of Kansas                 University of Kansas
> http://pj.freefaculty.org               http://quant.ku.edu
>
>
>

	[[alternative HTML version deleted]]

