-----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