[R-meta] Spatial-temporal meta-analysis
biorafaelrm at gmail.com
Thu Nov 16 14:18:59 CET 2017
Thank you for the amazing reply. I will read the references that you have
suggested and study more about those approaches. If you have more insights
about spatial-temporal meta-analysis, it will be gratifying read about it.
Rafael R. Moura.
Doutorando da Pós-graduação em Ecologia e Conservação de Recursos Naturais
Universidade Federal de Uberlândia, Uberlândia, MG, Brasil
Research Gate: https://www.researchgate.net/profile/Rafael_Rios_Moura2
2017-11-14 7:38 GMT-02:00 Viechtbauer Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl>:
> Meta-analytic models (like the ones that can be fitted with rma() and
> rma.mv()) are linear models. So, if you want to examine if the observed
> outcomes are related to time, then you could examine this by including an
> appropriate measure of time in your model as a predictors/moderator.
> Similarly, if you have some information on the location of the measurements
> and you want to examine if there is some relationship between the location
> and the size of the outcomes, this can be done via meta-regression. How
> best to do this in your particular case is a substantive question that I
> cannot answer.
> Alternatively, one could consider modeling space and time via random
> effects. For time, this might imply assuming an autoregressive structure
> for the random effects. rma.mv() includes random effects structures for
> AR1 and (just recently added) continuous-time autoregressive models. See
> help(rma.mv) and search for "AR" and "CAR" (for the latter, make sure you
> install the devel version of the metafor package:
> There are two datasets that illustrate the use of such structures: see
> help(dat.fine1993) and help(dat.ishak2007). It is useful to closely read
> the corresponding articles to get a better idea of how this works. There is
> Musekiwa, A., Manda, S. O., Mwambi, H. G., & Chen, D. G. (2016).
> Meta-analysis of effect sizes reported at multiple time points using
> general linear mixed model. PLOS ONE, 11(10), e0164898.
> which also illustrates this, using metafor.
> For space, you might need to use a spatial random effects structure. At
> the moment, this could be done by computing a distance matrix (assuming you
> have information on the location of the measurements) and turning this into
> a spatial covariance/correlation matrix, for example based on an
> exponential or Gaussian function; see: https://en.wikipedia.org/wiki/
> Covariance_function#Parametric_families_of_covariance_functions Then you
> can include the spatial correlation matrix via the 'R' argument in rma.mv().
> Since such spatial structures depend on one (or more) unknown parameters,
> you would have to do the optimization over that parameter manually (e.g.,
> trying out a bunch of values for the unknown parameters and finding the
> ones that maximizes the likelihood) or wrap rma.mv() in an extra
> optimization step. In the future, rma.mv() might include spatial
> structures directly.
> The best illustration of using the 'R' argument in rma.mv() I have at the
> moment shows how to do a phylogentic meta-analysis:
> That's something different than a spatial structure, but it illustrates
> how the 'R' argument works.
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
> >project.org] On Behalf Of Rafael Rios
> >Sent: Monday, 13 November, 2017 15:26
> >To: Michael Dewey; r-sig-meta-analysis at r-project.org
> >Subject: Re: [R-meta] Spatial-temporal meta-analysis
> >Dear Michael,
> >Thank you for the reply and assistence. I want to evaluate potential
> >spatio-temporal variation in effect size. Thus, I may built a random
> >effects meta-analysis to test if effect size changes (or it is
> >in space and time. I am having difficulty to choose an appropriate
> >to reach this goal.
> >Rafael R. Moura.
> >*scientia amabilis*
> >Doutorando da Pós-graduação em Ecologia e Conservação de Recursos
> >Universidade Federal de Uberlândia, Uberlândia, MG, Brasil
> >ORCID: http://orcid.org/0000-0002-7911-4734
> >Currículo Lattes:
> >Research Gate: https://www.researchgate.net/profile/Rafael_Rios_Moura2
> >2017-11-13 7:46 GMT-02:00 Michael Dewey <lists at dewey.myzen.co.uk>:
> >> Dear Rafael
> >> It might help to have just a sentence or two about the scientific
> >> here as I (and perhaps others) am not quite clear what your phrase
> >> "variation in average effect size is greater than zero" means.
> >> Michael
> >> On 12/11/2017 17:08, Rafael Rios wrote:
> >>> Dear Wolfgang,
> >>> Thank you for the attention. My data set follows on attached (CSV
> >>> When we found more than one season (three, for instance), we wrote "3"
> >>> the "season" column. Follow, we identified seasons as "1", "2" and "3"
> >>> the "class_season" column corresponding to each effect size. When
> >there was
> >>> only one season, we wrote "1" in the season column and "0" in the
> >>> class_season column. We adopted the same procedure for the "pop" and
> >>> "pop_class" columns corresponding to data about one or more
> >populations. We
> >>> also used as random variables "study" and "species". How may I do a
> >>> effects model to evaluate if variation in average effect size is
> >>> than zero?
> >>> Abraço,
> >>> Rafael R. Moura.
> >>> /scientia amabilis/
> >>> Doutorando da Pós-graduação em Ecologia e Conservação de Recursos
> >>> Universidade Federal de Uberlândia, Uberlândia, MG, Brasil
> >>> ORCID: http://orcid.org/0000-0002-7911-4734
> >>> Currículo Lattes: http://buscatextual.cnpq.br/bu
> >>> scatextual/visualizacv.do?id=K4244908A8
> >>> Research Gate: https://www.researchgate.net/profile/Rafael_Rios_Moura2
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