[R-meta] Post-hoc weighted analysis based on number of observations
Viechtbauer Wolfgang (SP)
wolfgang.viechtbauer at maastrichtuniversity.nl
Wed Jan 24 23:56:00 CET 2018
Dear Cesar,
Let me try to understand the essence of your question/issue and abstract it a bit from the specifics of your data. So, if I understand things correctly, you have data from various places on Earth. Let's pretend those places are on a 2d surface, so something like this (where * indicates a place where you have data):
+------------------------+
| * |
| * |
| * |
| * |
| * * |
| |
+------------------------+
You have fitted a model that relates an outcome to some predictor variables based on the data for these places. Now you actually have the values of the predictor variables for *all* places on that surface and you have computed the corresponding predicted values. But there are locations for which there were no data to begin with (e.g., upper right and lower left) and hence you want the SEs of the predicted values to reflect this lack of information in those areas and you are wondering how to do that. Does that capture the essence of your question?
Best,
Wolfgang
>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
>project.org] On Behalf Of Cesar Terrer Moreno
>Sent: Monday, 22 January, 2018 18:52
>To: r-sig-meta-analysis at r-project.org
>Subject: [R-meta] Post-hoc weighted analysis based on number of
>observations
>
>I have a gridded dataset representing the standard error (SE) of an
>effect. This SE was calculated through a meta-analysis and subsequent
>predictive model applied on a grid:
>
>ECMmeta <- rma(es, var, data=ecm.df ,control=list(stepadj=.5), mods= ~ 1
>+ MAP + MAT*CO2dif, knha=TRUE)
>options(na.action = "na.pass")
>ECMpred <- predict(ECMmeta,
> newmods = cbind(s.df$precipitation, s.df$temperature,
>CO2inc, s.df$temperature*CO2inc))
>ECMrelSE <- rasterFromXYZ(ECMpred[,c("x", "y", "se")],crs="+proj=longlat
>+datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
>
>I would like to add a further level of uncertainty to SE based on the
>number of measurements (observations) per type of ecosystem in the
>dataset. The idea is that ecosystems that are poorly represented by
>experiments in the dataset should have a higher SE than ecosystems with
>plenty of measurements in the dataset.
>
>I thought I could, for example, calculate an ecosystem-based weight as:
>
>weight = n/sum(n)
>
>That is, number of observations in a particular ecosystem divided by the
>total of observations.
>
>The next step would be to apply a weighting approach to each pixel. First
>approach I've come up with is to simply multiply SE and the inverse of
>the weight:
>
>SEw=SE*(1/weight)
>
>But the values are extremely high.
>
>An approach like this would be more like an post-hoc patch. I am sure
>something like this can be done within the meta-analysis at the
>beginning. Alternatively, a better post-hoc approach or ideas to
>investigate further would be welcome. Any recommendation or basic
>approach commonly used to add further uncertainty to areas with low
>representativeness?
>
>Thanks
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