I've got some data on discrete flood events (response variable - duration) on several rivers.

I've modelled the response variable by site and time using a Generalized Linear Model:

 mod = glm(duration ~ site + time)

I've then looked at the residual plots and cannot find any evidence for temporal correlation in the residuals. Does this mean that this model approach is valid (and that using a mixed/ random effects model is unnecessary) (or should one always use mixed/ random effects models for time-series data)?

Any links to reading references regarding this subject would be brilliant,

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    $\begingroup$ for what it's worth, this is really just a linear model (lm in R); since you're not specifying family, it defaults to Gaussian family. Technically you're right that it's a generalized linear model (since linear models are a subset of GLMs) but it may muddy the waters slightly. $\endgroup$ – Ben Bolker Oct 30 '13 at 13:12
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    $\begingroup$ I think you are somewhat equating Linear Mixed Effects models with Generalized Linear models and that also is a bit "odd"... Reference-wise: Stroup's "GLMM" (2013), Chapt. 2 "Design Matters" might be nice starting point. In general, as Pinheiro & Bates (2000) Chapt. 2 "LME model formulation" state: "(LME m.) extend linear models by incorporating random effects which can be regarded as additional error terms to account for correlation among observations within the group"; assuming you do not have such a grouping structure... well, you don't really need an LME, do you? :) $\endgroup$ – usεr11852 Nov 4 '13 at 23:17
  • $\begingroup$ Maybe I was a bit over-simplifying with me previous comment. Do you have any design related reasons to expect such a structure? If yes, maybe you could include it and probably investigate why it comes up so weak (it will also keep reviewers happy). Otherwise if nothing comes up I do not realistically see a reason to have an LME (or a GLMM for that matter). $\endgroup$ – usεr11852 Nov 4 '13 at 23:23

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