Code and dataset uploaded

R_code_Pu.R

@@ -0,0 +1,253 @@
+## Meta_final_code
+## by Pu
+## 2024May2
+
+
+library(metafor)
+library(readxl)
+metafin<-read_xlsx('C:/Users/cp/Desktop/Meta Analysis/Dataset_Pu.xlsx')
+
+## z adjustment
+metafin<-escalc("ZCOR",ri=ri, ni=ni,data=metafin,var.names = c('yzi','vzi'))
+
+## zero effect size
+randem_zero<- rma(yzi,vzi, data= metafin, slab = author) 
+randem_zero
+
+predict(randem_zero, digits=3, transf = transf.ztor)
+confint(randem_zero)
+
+## funnel plot
+funnel(randem_zero)
+
+## forest plot
+forest(randem_zero, xlim = c(-1.2,1.2),atransf=transf.ztor,
+       digits = c(2,1),
+       cex=.6)
+text(-1.2,24,"Authors(year)",pos=4, cex=1)
+text(1.2,24,'r[95% CI]',pos=2,cex=1)
+
+## small study effect test
+regtest(randem_zero)
+
+## influential case test
+inf<- influence(randem_zero)
+print(inf)
+plot(inf)
+
+## contour funnel plot
+metafor::funnel(randem_zero, level=c(90, 95, 99), shade=c("white", "gray55", "gray75"), refline=0,
+                xlab= "r",legend=TRUE)
+
+## number of authors
+summary(metafin$nauthor)
+sd(metafin$nauthor)
+
+metafin$nau4<-0
+metafin$nau4[metafin$nauthor>=4]<-1
+table(metafin$nau4)
+
+randem_nau4<- rma(yzi,vzi, mods= ~nau4,data= metafin, slab = author) 
+randem_nau4
+
+## moving constant
+metafin$invernau4<-1-metafin$nau4
+randem_invernau4<- rma(yzi,vzi, mods= ~invernau4,data= metafin, slab = author) 
+randem_invernau4
+
+## cite
+summary(metafin$cite)
+sd(metafin$cite)
+
+randem_cite<-rma(yzi,vzi, mods= ~cite,data= metafin, slab = author) 
+randem_cite
+
+## survey length
+summary(metafin$days)
+sd(metafin$days,na.rm=TRUE)
+randem_days<-rma(yzi,vzi, mods= ~days,data= metafin, slab = author) 
+randem_days
+
+metafin$tw<-NA
+metafin$tw[metafin$days>=14]<-1
+metafin$tw[metafin$days<14& metafin$days>0]<-0
+summary(metafin$tw)
+
+## interpolation
+metafin$twitpl<-1
+metafin$twitpl[metafin$days<14& metafin$days>0]<-0
+
+randem_tw<-rma(yzi,vzi, mods= ~tw,data= metafin, slab = author) 
+randem_tw
+
+randem_twitpl<-rma(yzi,vzi, mods= ~twitpl,data= metafin, slab = author) 
+randem_twitpl
+
+## moving constant
+metafin$invertwitpl<-1-metafin$twitpl
+randem_invertwitpl<-rma(yzi,vzi, mods= ~invertwitpl,data= metafin, slab = author) 
+randem_invertwitpl
+
+## nationwide
+metafin$nw<-0
+metafin$nw[metafin$scale=="nationwide"]<-1
+
+summary(metafin$nw)
+sd(metafin$nw)
+randem_nw<-rma(yzi,vzi, mods= ~nw,data= metafin, slab = author) 
+randem_nw
+
+## quota
+metafin$quota<-0
+metafin$quota[metafin$sampling=="online quota"]<-1
+summary(metafin$quota)
+sd(metafin$quota)
+randem_quota<-rma(yzi,vzi, mods= ~quota,data= metafin, slab = author) 
+randem_quota
+
+## average age
+metafin$age40<-0
+metafin$age40[metafin$aveage<40]<-1
+summary(metafin$aveage)
+sd(metafin$aveage)
+randem_age<- rma(yzi,vzi, mods= ~aveage,data= metafin, slab = author) 
+randem_age
+
+randem_age40<- rma(yzi,vzi, mods= ~age40,data= metafin, slab = author) 
+randem_age40
+
+## female percent
+summary(metafin$femper)
+sd(metafin$femper)
+randem_femper<- rma(yzi,vzi, mods= ~femper,data= metafin, slab = author) 
+randem_femper
+
+## average education
+summary(metafin$aveedu)
+sd(metafin$aveedu,na.rm=TRUE)
+randem_edu<- rma(yzi,vzi, mods= ~aveedu,data= metafin, slab = author) 
+randem_edu
+
+## EU/US nation
+table(metafin$nation)
+
+metafin$euus<-0
+metafin$euus[metafin$nation=='Finland'|metafin$nation=='France'|metafin$nation=='UK'|
+               metafin$nation=='Czech'|metafin$nation=='Poland'|metafin$nation=='US'|metafin$nation=='Italy'
+             |metafin$nation=='Serbia'|metafin$nation=='Slovakia']<-1
+randem_euus<-rma(yzi,vzi, mods= ~euus,data= metafin, slab = author) 
+randem_euus
+## moving constant
+metafin$inveuus<-1-metafin$euus
+randem_euus<-rma(yzi,vzi, mods= ~inveuus,data= metafin, slab = author) 
+randem_euus
+
+## number of controls
+summary(metafin$ncontrol)
+sd(metafin$ncontrol)
+randem_ncon<-rma(yzi,vzi, mods= ~ncontrol,data= metafin, slab = author) 
+randem_ncon
+
+## make plots
+predncon<-predict(randem_ncon, newmods = seq(0,30,1))
+ncons<- 1/sqrt(metafin$vzi)
+nconsize<- (ncons-min(ncons)/(max(ncons)-min(ncons)))/30
+plot(metafin$ncontrol,metafin$yzi,xlab = 'Numbers of Controls',
+     ylab = "Correlation Coefficient (R)",xlim = c(0,30),ylim=c(0,0.8),cex=nconsize)
+lines(seq(0,30,1), predncon$pred, col = "navy")
+lines(seq(0,30,1), predncon$ci.lb, lty = "dashed", col=" blue")
+lines(seq(0,30,1), predncon$ci.ub, lty = "dashed", col=" blue")
+abline(h=0,col='red')
+
+## months since 2020.1
+metafin$monnumber<-metafin$month+(metafin$year-2020)*12
+summary(metafin$monnumber)
+sd(metafin$monnumber,na.rm = TRUE)
+
+## interpolation
+metafin$monnumberitpl<-metafin$monnumber
+metafin$monnumberitpl[which(is.na(metafin$monnumberitpl))]<-8.579
+summary(metafin$monnumberitpl)
+
+randem_mon<-rma(yzi,vzi, mods= ~monnumber,data= metafin, slab = author) 
+randem_mon
+
+randem_monitpl<-rma(yzi,vzi, mods= ~monnumberitpl,data= metafin, slab = author) 
+randem_monitpl
+
+## plot
+prednmonitpl<-predict(randem_monitpl, newmods = seq(0,20,1))
+ncons<- 1/sqrt(metafin$vzi)
+nconsize<- (ncons-min(ncons)/(max(ncons)-min(ncons)))/30
+plot(metafin$monnumber,metafin$yzi,xlab = 'Numbers of Month Since 2020 Jan.',
+     ylab = "Correlation Coefficient (R)",xlim = c(0,20),ylim=c(0,0.8),cex=nconsize)
+lines(seq(0,20,1), prednmonitpl$pred, col = "navy")
+lines(seq(0,20,1), prednmonitpl$ci.lb, lty = "dashed", col=" blue")
+lines(seq(0,20,1), prednmonitpl$ci.ub, lty = "dashed", col=" blue")
+abline(h=0,col='red')
+
+## type of conspiracy
+table(metafin$xcontent)
+metafin$xcontb<-factor(metafin$xcontent,
+                       levels = c('Political interests','vaccine','mixed','Bioweapon/man made','Hidden truth','hoax'))
+randem_xcontb<-rma(yzi,vzi, mods= ~xcontb,data= metafin, slab = author) 
+randem_xcontb
+
+predxcontp<-predict(randem_xcontb, newmods = c(0,0,0,0,0))
+predxcontp$pred
+predxcontp$ci.lb
+predxcontp$ci.ub
+
+predxcontv<-predict(randem_xcontb, newmods = c(1,0,0,0,0))
+predxcontv$pred
+predxcontv$ci.lb
+predxcontv$ci.ub
+
+predxcontm<-predict(randem_xcontb, newmods = c(0,1,0,0,0))
+predxcontm$pred
+predxcontm$ci.lb
+predxcontm$ci.ub
+
+predxcontb<-predict(randem_xcontb, newmods = c(0,0,1,0,0))
+predxcontb$pred
+predxcontb$ci.lb
+predxcontb$ci.ub
+
+predxcontht<-predict(randem_xcontb, newmods = c(0,0,0,1,0))
+predxcontht$pred
+predxcontht$ci.lb
+predxcontht$ci.ub
+
+predxcontho<-predict(randem_xcontb, newmods = c(0,0,0,0,1))
+predxcontho$pred
+predxcontho$ci.lb
+predxcontho$ci.ub
+
+table(metafin$xcontent)
+
+metafin$xcontnum<-0
+metafin$xcontnum[metafin$xcontent=='Political interests']<-1
+metafin$xcontnum[metafin$xcontent=='vaccine']<-2
+metafin$xcontnum[metafin$xcontent=='mixed']<-3
+metafin$xcontnum[metafin$xcontent=='Bioweapon/man made']<-4
+metafin$xcontnum[metafin$xcontent=='Hidden truth']<-5
+metafin$xcontnum[metafin$xcontent=='hoax']<-6
+
+## plot
+plot(metafin$xcontnum,metafin$yzi,xlab = 'Type of Conspiracy',
+     ylab = "Correlation Coefficient (R)",ylim=c(-0.3,1.1),cex=nconsize)
+legend('topleft',cex=0.8,
+       c('1=Political interests','2=Vaccine','3=Mixed','4=Bioweapon/man made','5=Hidden truth','6=Hoax'))
+lines(c(1,1,1),c(predxcontp$ci.lb,predxcontp$pred,predxcontp$ci.ub),lty=2,col='grey')
+lines(c(2,2,2),c(predxcontv$ci.lb,predxcontv$pred,predxcontv$ci.ub),lty=2,col='grey')
+lines(c(3,3,3),c(predxcontm$ci.lb,predxcontm$pred,predxcontm$ci.ub),lty=2,col='grey')
+lines(c(4,4,4),c(predxcontb$ci.lb,predxcontb$pred,predxcontb$ci.ub),lty=2,col='grey')
+lines(c(5,5,5),c(predxcontht$ci.lb,predxcontht$pred,predxcontht$ci.ub),lty=2,col='grey')
+lines(c(6,6,6),c(predxcontho$ci.lb,predxcontho$pred,predxcontho$ci.ub),lty=2,col='grey')
+points(1,predxcontp$pred,pch=1,lwd=5,col='grey')
+points(2,predxcontv$pred,pch=1,lwd=5,col='grey')
+points(3,predxcontm$pred,pch=1,lwd=5,col='grey')
+points(4,predxcontb$pred,pch=1,lwd=5,col='grey')
+points(5,predxcontht$pred,pch=1,lwd=5,col='grey')
+points(6,predxcontho$pred,pch=1,lwd=5,col='grey')
+abline(h=0,col='red')