### Data
h_mc=read.csv2('mutual_mate_choice_only.csv', dec='.')

### Model
meta=rma.mv(zf, sezf, mods=~mate_choice, random = list (~1|studyID, ~1|species1, ~1|potential_sce), data = h_mc)

### There is a significant p-value for z-test of the treatment 1.
### Why is not p-value of QM-test significant?
print(meta, 3)

### Why is IČ a negative value?
#QE(df = 102) = 106.866, p-val = 0.351
100*(102-106.866)/106.866 #IČ

#Is it possible to do pairwise comparisons when QM-test is not significant?
library(multcomp)

summary(glht(meta, linfct=rbind(female=c(1,0,0), male=c(0,1,0))), test=Chisqtest())
summary(glht(meta, linfct=rbind(female=c(1,0,0), mutual=c(0,0,1))), test=Chisqtest())
summary(glht(meta, linfct=rbind(mutual=c(0,0,1), male=c(0,1,0))), test=Chisqtest())

# Pairwise comparisons show higher outcomes that male and mutual mate choice, besides QM-test results. What is the most trustful result? 
# Is there an alternative test to evaluate the significance of the moderator?

### Graph

y<-summary(meta)$b
ci_l<-summary(meta)$ci.lb
ci_h<-summary(meta)$ci.ub

fg<-data.frame(cbind(y,ci_l,ci_h))
colnames(fg)[1]<-"y"
colnames(fg)[2]<-"ci_l"
colnames(fg)[3]<-"ci_h"
fg$mate_choice<-c("female","male","mutual")
fg$mate_choice<-as.factor(fg$mate_choice)

fg


library(ggplot2)

a<-ggplot(fg,aes(x=mate_choice,y=y,ymax=ci_h,ymin=ci_l,size=0.1), shape = 16)
b<-a+geom_pointrange(size=1.5)
d=b+theme_classic()+
  theme(axis.text=element_text(size=13, color="black"),axis.title=element_text(size=16))
e<-d+ xlab('Mate choice') +ylab ("Effect Size (Fisher's Z)")
f=e+geom_hline(aes(yintercept=0), lty=2,size=1)
f