[R-sig-ME] Robust SEs in GLMMs

Chris Howden chris at trickysolutions.com.au
Thu Nov 27 03:18:18 CET 2014

Hi Sharon,

I faced the same problems a few years ago and tried using one of R's GAM
packages to calculate robust SE's. At the time we couldn’t get it to work
and I believe it was because we had too much data, so R ran into memory

Some other references you might like to have a look at are below. I
haven’t looked at them in detail myself yet, but as I've got a big data
set I intend to do this type of analysis on one day I've got a bit of "To
Read" list going

Pang (2010) Modeling Heterogeneity and Serial Correlation in Binary
Timeseries Crosssectional data. A Bayesian multilevel model with ARp

Jay Ver Hoef's package glmmLDTS .  I think it deals with all your issues
(binomial, random effects, temporal autocorrelation...but not GAMM). It
may only run on < R.3.0):
*Ver Hoef, J.M.*, London, J.M., and Boveng, P.L.  2010.  Fast computing of
some generalized linear mixed pseudo-models with temporal autocorrelation.
*Computational Statistics **25*(1)*: *39 - 55*. * DOI:
London, J.M., *Ver Hoef, J.M.*, Jeffries, S.J., Lance, M.M., and Boveng,
P.L. 2012. Haul-out behavior of harbor seals (Phoca vitulina) in Hood
Canal, Washington.  *PLoS ONE **7*(6):e38180.

Chris Howden B.Sc. (Hons) GStat.
Founding Partner
Data Analysis, Modelling and Training
Evidence Based Strategy/Policy Development, IP Commercialisation and
(mobile) +61 (0) 410 689 945
(skype) chris.howden
chris at trickysolutions.com.au

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-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Sharon
Sent: Monday, 24 November 2014 8:05 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Robust SEs in GLMMs

When computing resource selection functions for animal telemetry data with
a binary response variable, where the 1s represent animal location data,
which are spatially and temporally correlated, and the 0s represent random
locations, which are not correlated, it is recommended to calculate
robust, or empirical, standard errors instead of using the model-based
standard errors to account for this differing correlation structure.  As
far as I can tell, none of the glmm packages in R calculate these robust
SEs.  Does anyone know of a way to use glmms that calculate these?


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