-----Original Message-----
From: Grace Pold [mailto:apold at umass.edu]
Sent: Friday, 21 August, 2020 5:21
To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis at r-project.org <r-sig-
meta-analysis at r-project.org>
Subject: RE: metafor - specifying spatial random effects
Dear Wolfgang,
I have a follow-up question.
When comparing the spatial and the non-spatial models, I have different
parameter estimates as expected. However, the residuals are identical for
the two models. Is it expected that there would be the same residuals for
the spatial and non-spatial models?
Thank you for your assistance,
Grace
From: Viechtbauer, Wolfgang (SP)
Sent: Thursday, August 20, 2020 4:28 AM
To: Grace Pold; r-sig-meta-analysis at r-project.org <r-sig-meta-analysis at r-
project.org>
Subject: RE: metafor - specifying spatial random effects
Dear Grace,
you need to create a variable that is a constant, so:
datasub$const <- 1
and then you can use:
random = ~ Longitude + Latitude | const
The post you are referring to is outdated. At that time, the rma.mv()
function did not yet have spatial correlation structures. Now you can just
use the 'struct' argument to specify the spatial correlation structure and
the optimization is done for you.
With respect to your second question: To me, the choice (whether to include
location as a fixed or a random effect) comes down to whether I am actually
interested in differences between the specific locations that make up the
spatial configuration of your data points (in which case I would use a fixed
effect) or I just want to account for possible dependency due to the spatial
configuration (in which case I would use a random effect).
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
On Behalf Of Grace Pold
Sent: Thursday, 20 August, 2020 0:41
To: r-sig-meta-analysis at r-project.org <r-sig-meta-analysis at r-project.org>
Subject: [R-meta] metafor - specifying spatial random effects
Hello,
I have a dataset of effect sizes taken at different places in the northern
hemisphere, sometimes in close proximity, and sometimes in distant
locations.
I have two questions. The first is specifically related to metafor and
specification, and the second is more generally related to spatial random
effects (which I don?t think I will get answered, but thought I would throw
it in anyway).
First, I couldn?t find any examples of how to specify the model to include
spatial random effects outside of the function help text. So I was hoping
someone could tell me if the following specification for a geographic
distance model is correct:
rma.mv(yi=yi, V=vi, mods=~moderator1, random = ~ Longitude+ Latitude|
moderator1,data=datub,method="REML", dist="gcd", struct="SPEXP")
when there is a moderator like habitat type in my analysis. And:
rma.mv(yi=yi, V=vi, random = ~ Longitude+Latitude|StudyID,
data=datasub,method="REML", dist="gcd", struct="SPEXP")
when I do not include moderators (StudyID is just the unique datapoint ID
and so each StudyID only has one effect size value associated with it). I
chose gcd as the distance because the curvature of the earth matters in my
data, and have no rationale for choosing SPEXP for structure.
The help text says ?Let?d?denote the distance between two points that share
the same level of the?outer?variable (if all true effects are allowed to be
spatially correlated, simply set?outer?to a constant)?, so intuitively I
wanted to write the model as
rma.mv(yi=yi, V=vi, random = ~ Longitude+Latitude|1,
data=datasub,method="REML", dist="gcd", struct="SPEXP")
(ie with a ?1? as the ?outer? variable rather than a unique datapoint ID)
because there is no ?distance between two points sharing the same level? of
StudyID because those are unique. However, it gives me a ?Error in
eval(predvars, data, env) : object 'Latitude' not found? error unless I
include one of the named variables.
I also wanted to check that the ?parameter optimization? mentioned in the
blog post here [https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2017-
November/000371.html] on a previous approach to including spatial
matrices is accounted for in the most recent version of metafor. Or are
there additional steps I would need to complete?
Second, if you wanted to know whether to include geographic location as a
categorical (ex. New York vs. Beijing) versus geographic distance random
effect versus not at all, would you suggest extracting the residuals and
checking to see which model has done the best job of removing spatial
correlation, if any exists, using Moran?s I?
Thank you so much for your time and input,
Grace Pold
Postdoctoral researcher
Cal Poly NRES