# varrd.s : Variance- based method for risk difference # dat<-scan("c:/splus/beta2.txt",list(a=0,n1=0,c=0,n0=0,d=0)) k<-length(dat$a) # # parameters for figures xposi <- 0.12 # x-axis for titles yup <- 20 # upper limit of y-axis ylow <- -3 # lower limit of y-axis xup <- 0.2 # upper limit of x-axis xlow <- -0.2 # lower limit of x-axis # # par(mar=c(5,4,4,3)) ai<-dat$a bi<-dat$n1-dat$a ci<-dat$c di<-dat$n0-dat$c tn<-dat$n1+dat$n0 n1<-dat$n1 n0<-dat$n0 rd<- (ai/n1) - (ci/n0) se<-sqrt(ai*bi/n1^3 + ci*di/n0^3) low<-rd-1.96*se upp<-rd+1.96*se # ---------- individual graph ------------------ id<-k:1 plot(rd,id,ylim=c(ylow, yup), xlim=c(xlow, xup),yaxt="n",pch=" ", ylab="Citation",xlab="Risk difference") title(main=" Variance-based method ") symbols(rd,id,squares=sqrt(tn), add=T,inches=0.20) abline(v=0) for (i in 1:k){ j<-k-i+1 x<-c(low[i],upp[i]) y<-c(j,j) lines(x,y,type="l") text(xposi ,i,j) } # ----- fixed effects ---- w<-1/se/se sw<-sum(w) varrd<- sum(rd*w)/sw varrdl<- varrd -1.96*sqrt(1/sw) varrdu<- varrd +1.96*sqrt(1/sw) q1<-sum( w*(rd-varrd)^2 ) df1<-k-1 pval1<- 1-pchisq(q1,df1) q2<-varrd^2*sw df2<-1 pval2<- 1-pchisq(q2,df2) # ----- random-effects ---- tau2<-(q1-(k-1))/(sw-sum(w*w)/sw) tau2<-max(0,tau2) wx<-1/(tau2+se*se) swx<-sum(wx) varrdd<-sum(rd*wx)/swx varrddl<- varrdd-1.96*sqrt(1/swx) varrddu<- varrdd+1.96*sqrt(1/swx) qx2<-varrdd^2*swx pvalx2<- 1-pchisq(qx2,df2) # -------- graph ---------------- x<-c(varrdl,varrdu) y<-c(-1,-1) lines(x,y,type="b") x<-c(varrddl,varrddu) y<-c(-2,-2) lines(x,y,type="b") abline(v=c(varrd,varrdd), lty=2) text(xposi,-1, "Combined : fixed") text(xposi,-2, "Combined : random") # ------ output variables ------- out <- round( c(varrd, varrdl, varrdu), 4) outq1<- round( c(q1, df1, pval1), 6) outq2<- round( c(q2, df2, pval2), 6) outR<- round( c(varrdd, varrddl, varrddu), 4) outq2R<- round( c(qx2, df2, pvalx2), 6)