[R-sig-ME] Multi-level models for nested variables in time dimension

Phillip Alday me @end|ng |rom ph||||p@|d@y@com
Sat Dec 4 05:27:23 CET 2021

The math of mixed models doesn't care whether the original dimension was
space, time or something else entirely. In both time and space, you can
have autocorrelation that messes with model assumptions, but it's that
autocorrelation that matters more than the physical interpretation of
the dimension.

All that said, it's incumbent on the user to know what the inferential
interpretation of the resulting model is. Methods designed to deal with
serial autocorrelation may have a more obvious interpretation. But the
question of which model gives you the inferences you need is one that
requires the knowledge of your data and research question that only you

Hope that helps,


On 11/19/21 18:21, Vitor Vieira Vasconcelos wrote:
> Good night, friends!
>      I have been seeing many multi-level models, using the mixed models'
> framework, that use groups nested in "space", such as students nested in
> classes, which are nested in schools, and with independent variables for
> each of these spatial resolutions.
>     Then I was thinking whether we could use this same framework to model
> variables nested in the time dimension. For example, if we have some
> variables sampled at daily resolution, other variables at monthly
> resolution and others at year resolution, and we would like to use all them
> in the same model to predict a dependent variable at daily resolution.
>    Basically, I am just thinking about transposing the same framework from
> "space" dimension to "time" dimension, and not thinking yet about
> autocorrelation or other time-series analyses.
>     Do you think that these ideas make sense to you?
> Best regards,
> Vitor Vieira Vasconcelos
> +55-31-99331-1593
> 	[[alternative HTML version deleted]]
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