library(metafor)
library(Matrix)
library(rJava)


data1<-read.csv(file.choose(),header=T)

## calculate effect size, lnRR

eff1<-escalc(measure="ROM",data=data1,m1i=treatment, sd1i=SD,n1i=N,m2i=control,sd2i=CSD,n2i=CN)

## calculate the Qm of moderators
# for Categorical variables

r1<-rma(yi,vi, mods=~ecosystem, data=eff1, method="REML")

# for numeric variables

r2<-rma(yi,vi, mods=~SOC,data=eff1, method="REML")

## model selection

library(glmulti)
rma.glmulti <- function(formula, data, ...) { rma(formula, vi, data=data, method="ML", ...)}

modelselection<- glmulti(yi ~ DT+soiltexture+ecosystem+CNratio+frequency+TN+SOC+climate, data=eff1, level=1, fitfunction=rma.glmulti, crit="aicc", confsetsize=256)

plot(modelselection, type="s")