[R-meta] escale ROM or SMD
Lukas Dylewski
dy|ew@k|91 @end|ng |rom gm@||@com
Mon Jul 12 12:43:27 CEST 2021
Dear All,
I conducted a mixed meta-regression model to check the effect of drought
stress on proline concentration in leaves. In the model, I include the
following moderators: duration of drought (in the day), seed mass, and
plant type (coniferous vs. deciduous). The duration of the drought affects
the proline content, therefore it was included in the model.
When I calculate Hedge g (measure=SMD) values I got very big values for
some records (e.g. 100, 50, etc.) and publication bias. However, when I
calculate effect size using ROM I got very nice numbers without publication
bias.
My first question: Can I use the ROM method for my dataset (I attach the
database: proline) or should I use the SMD?
This is my code (with ROM measure)
z<-proline
z$logmass<-log(z$seed.mass.mg+1)
hedges<-escalc(measure="ROM",data=z,append=T,m1i=experiment,n1i=n.experiment,sd1i=sd.experiment,m2i=control,n2i=n.control,sd2i=sd.control)
hedges
res1 <- rma.mv(yi, vi, mods = ~ logmass + I(logmass^2)+ factor(plant.type)
+ drougth.day + I(drougth.day^2), random=~1|references,data=hedges,
method="REML")
After the model summary, I would like to calculate the mean effect size for
the plant type (coniferous and deciduous separately). Does this code
correct?
m <- mean(hedges$logmass)
n <- mean(hedges$drougth.day)
For conifeorus:
predict(res1, newmods = c(m, m^2, 0,n,n^2))
For deciduous:
predict(res1, newmods = c(m, m^2, 1,n,n^2))
--
Łukasz Dylewski, PhD.
Institute of Dendrology,
Polish Academy of Sciences,
Parkowa 5, 62-035 Kórnik, Poland
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