heterogeneitytablesimulation

Heterogeneity Analysis: Shared Structured, Sharing by sign

data=readRDS("../../../Dropbox/simdata.rds")
t=data$tstat;bhat=data$betahat;sebetahat=data$sebetahat;beta=data$beta;v.j=matrix(rep(1,ncol(t)*nrow(t)),ncol=ncol(t),nrow=nrow(t))

mash.means=read.table("../../../Dropbox/simulationsmay12/shared/sharedashcutoffomega2jun15posterior.means.txt")[,-1]

bma.means=read.table("../../../Dropbox/simulationsmay12/shared/noashsharedwithzerobmaallposterior.means.txt")[,-1]

ash.means=read.table("../../../Dropbox/simulationsmay12/shared/univariate.ash.pm.txt")


lfsr.mash=read.table("../../../Dropbox/simulationsmay12/shared/sharedashcutoffomega2jun15lfsr.txt")[,-1]
lfsr.bma=read.table("../../../Dropbox/simulationsmay12/shared/noashsharedwithzerobmaalllfsr.txt")[,-1]
lfsr.ash=read.table("../../../Dropbox/simulationsmay12/shared/univariate.ash.lfsr.txt")



standard.error=data$sebetahat

pm.mash.beta=mash.means*standard.error
pm.bma.beta=bma.means*standard.error
pm.ash.beta=ash.means*standard.error

thresh=0.05

Here, we show the Proportion of Sharing by Sign:

sigmat=(lfsr.mash<=thresh)
nsig= rowSums(sigmat)
#(signall=mean(het.norm(pm.mash.beta[nsig>0,])>0))
(signall.mash=mean(het.norm(pm.mash.beta[1:400,])>0))

## [1] 0.7284659

##BMA
sigmat=(lfsr.bma<=thresh)
nsig= rowSums(sigmat)
#(signall=mean(het.norm(pm.bma.beta[nsig>0,])>0))
(signall.bma=mean(het.norm(pm.bma.beta[1:400,])>0))

## [1] 0.7469318

##ASH
sigmat=(lfsr.ash<thresh)
nsig= rowSums(sigmat)
#(signall=mean(het.norm(pm.ash.beta[nsig>0,])>0))
(signall.ash=mean(het.norm(pm.ash.beta[1:400,])>0))

## [1] 0.7119318

####SHow that results are robust in specific analysis

(truth=(mean(het.norm(data$beta[1:400,])>0)))

## [1] 0.7269318

(standard=mean(het.norm(data$betahat[1:400,])>0))

## [1] 0.7111364

(RMLE=sqrt(mean((standard-truth)^2)))

## [1] 0.01579545

#RMLE=1
(RRMSE.mash=sqrt(mean((signall.mash-truth)^2))/RMLE)

## [1] 0.0971223

(RRMSE.bma=sqrt(mean((signall.bma-truth)^2))/RMLE)

## [1] 1.266187

(RRMSE.ash=sqrt(mean((signall.ash-truth)^2))/RMLE)

## [1] 0.9496403

rmse.all.table=cbind(mash=RRMSE.mash,bmalite=RRMSE.bma,ash=RRMSE.ash)
barplot(as.numeric(rmse.all.table),main="Shared, Structured Effects: Sign heterogeneity",
        ylab="relative error (RRMSE)",xlab="Method",col=c("green","blue","red"),names=colnames(rmse.all.table),ylim=c(0,1.5),cex.main=1.5,cex.lab=1,cex.names=1,las=2)

Heterogeneity Analysis: Independent SSimulation by Sign

data=readRDS("../../../Dropbox/simulationsmarch9/independentsim/independentsim.rds")
t=data$tstat;bhat=data$betahat;sebetahat=data$sebetahat;beta=data$beta;v.j=matrix(rep(1,ncol(t)*nrow(t)),ncol=ncol(t),nrow=nrow(t))


mash.means=read.table("../../../Dropbox/simulationsmay12/independentsim_all/independentsimashcutoffomega2jun15posterior.means.txt")[,-1]
ash.means=read.table("../../../Dropbox/simulationsmay12/independentsim_all/univariate.ash.pmind.txt")

bma.means=read.table("../../../Dropbox/simulationsmay12/independentsim_all/noashindependentwithzerobmaallposterior.means.txt")[,-1]



lfsr.mash=read.table("../../../Dropbox/simulationsmay12/independentsim_all/independentsimashcutoffomega2jun15lfsr.txt")[,-1]
lfsr.bma=read.table("../../../Dropbox/simulationsmay12/independentsim_all/noashindependentwithzerobmaalllfsr.txt")[,-1]
lfsr.ash=read.table("../../../Dropbox/simulationsmay12/independentsim_all/univariate.ashind.lfsr.txt")




standard.error=data$sebetahat

pm.mash.beta=mash.means*standard.error
pm.bma.beta=bma.means*standard.error
pm.ash.beta=ash.means*standard.error

thresh=0.05

Here, we show the Proportion of Sharing by Sign:

sigmat=(lfsr.mash<=thresh)
nsig= rowSums(sigmat)
#(signall=mean(het.norm(pm.mash.beta[nsig>0,])>0))
(signall.mash=mean(het.norm(pm.mash.beta[1:400,])>0))

## [1] 0.5125

##BMA
sigmat=(lfsr.bma<=thresh)
nsig= rowSums(sigmat)
#(signall=mean(het.norm(pm.bma.beta[nsig>0,])>0))
(signall.bma=mean(het.norm(pm.bma.beta[1:400,])>0))

## [1] 0.5119886

##ASH
sigmat=(lfsr.ash<thresh)
nsig= rowSums(sigmat)
#(signall=mean(het.norm(pm.ash.beta[nsig>0,])>0))
(signall.ash=mean(het.norm(pm.ash.beta[1:400,])>0))

## [1] 0.5119886

(truth=(mean(het.norm(data$beta[1:400,])>0)))

## [1] 0.5127841

(standard=mean(het.norm(data$betahat[1:400,])>0))

## [1] 0.5107386

(RMLE=sqrt(mean((standard-truth)^2)))

## [1] 0.002045455

(RRMSE.mash=sqrt(mean((signall.mash-truth)^2))/RMLE)

## [1] 0.1388889

(RRMSE.bma=sqrt(mean((signall.bma-truth)^2))/RMLE)

## [1] 0.3888889

(RRMSE.ash=sqrt(mean((signall.ash-truth)^2))/RMLE)

## [1] 0.3888889

rmse.all.table=cbind(mash=RRMSE.mash,bmalite=RRMSE.bma,ash=RRMSE.ash)
barplot(as.numeric(rmse.all.table),main="Shared, Unstructured Effects: Sign heterogeneity",
        ylab="relative error (RRMSE)",xlab="Method",col=c("green","blue","red"),names=colnames(rmse.all.table),ylim=c(0,1.5),cex.main=1.5,cex.lab=1,cex.names=1,las=2)

Heterogeneity Analysis: Shared,Structured by magnitude

data=readRDS("../../../Dropbox/simdata.rds")
t=data$tstat;bhat=data$betahat;sebetahat=data$sebetahat;beta=data$beta;v.j=matrix(rep(1,ncol(t)*nrow(t)),ncol=ncol(t),nrow=nrow(t))

mash.means=read.table("../../../Dropbox/simulationsmay12/shared/sharedashcutoffomega2jun15posterior.means.txt")[,-1]
ash.means=read.table("../../../Dropbox/simulationsmay12/shared/univariate.ash.pm.txt")
bma.means=read.table("../../../Dropbox/simulationsmay12/shared/noashsharedwithzerobmaallposterior.means.txt")[,-1]



lfsr.mash=read.table("../../../Dropbox/simulationsmay12/shared/sharedashcutoffomega2jun15lfsr.txt")[,-1]
lfsr.bma=read.table("../../../Dropbox/simulationsmay12/shared/noashsharedwithzerobmaalllfsr.txt")[,-1]
lfsr.ash=read.table("../../../Dropbox/simulationsmay12/shared/univariate.ash.lfsr.txt")



standard.error=data$sebetahat

pm.mash.beta=mash.means*standard.error
pm.bma.beta=bma.means*standard.error
pm.ash.beta=ash.means*standard.error

thresh=0.05

Here, we show the Proportion of Sharing by Sign:

sigmat=(lfsr.mash<=thresh)
nsig= rowSums(sigmat)
(signall=mean(het.norm(pm.mash.beta[nsig>0,])>0.5))

## [1] 0.2848853

(signall.mash=mean(het.norm(pm.mash.beta[1:400,])>0.5))

## [1] 0.2440909

##BMA
sigmat=(lfsr.bma<=thresh)
nsig= rowSums(sigmat)
(signall=mean(het.norm(pm.bma.beta[nsig>0,])>0.5))

## [1] 0.3696412

(signall.bma=mean(het.norm(pm.bma.beta[1:400,])>0.5))

## [1] 0.2941477

##ASH
sigmat=(lfsr.ash<thresh)
nsig= rowSums(sigmat)
(signall=mean(het.norm(pm.ash.beta[nsig>0,])>0.5))

## [1] 0.1679426

(signall.ash=mean(het.norm(pm.ash.beta[1:400,])>0.5))

## [1] 0.1315909

####SHow that results are robust in specific analysis

(truth=(mean(het.norm(data$beta[1:400,])>0.5)))

## [1] 0.2479545

(standard=mean(het.norm(data$betahat[1:400,])>0.5))

## [1] 0.2152273

(RMLE=sqrt(mean((standard-truth)^2)))

## [1] 0.03272727

(RRMSE.mash=sqrt(mean((signall.mash-truth)^2))/RMLE)

## [1] 0.1180556

(RRMSE.bma=sqrt(mean((signall.bma-truth)^2))/RMLE)

## [1] 1.411458

(RRMSE.ash=sqrt(mean((signall.ash-truth)^2))/RMLE)

## [1] 3.555556

rmse.all.table=cbind(mash=RRMSE.mash,bmalite=RRMSE.bma,ash=RRMSE.ash)
barplot(as.numeric(rmse.all.table),main="Shared, Structured Effects: Magnitude heterogeneity",
        ylab="relative error (RRMSE)",xlab="Method",col=c("green","blue","red"),names=colnames(rmse.all.table),ylim=c(0,1.5),cex.main=1.5,cex.lab=1,cex.names=1,las=2)

Heterogeneity Analysis: Independent by magnitude

data=readRDS("../../../Dropbox/simulationsmarch9/independentsim/independentsim.rds")
t=data$tstat;bhat=data$betahat;sebetahat=data$sebetahat;beta=data$beta;v.j=matrix(rep(1,ncol(t)*nrow(t)),ncol=ncol(t),nrow=nrow(t))


mash.means=read.table("../../../Dropbox/simulationsmay12/independentsim_all/independentsimashcutoffomega2jun15posterior.means.txt")[,-1]
ash.means=read.table("../../../Dropbox/simulationsmay12/independentsim_all/univariate.ash.pmind.txt")

bma.means=read.table("../../../Dropbox/simulationsmay12/independentsim_all/noashindependentwithzerobmaallposterior.means.txt")[,-1]



lfsr.mash=read.table("../../../Dropbox/simulationsmay12/independentsim_all/independentsimashcutoffomega2jun15lfsr.txt")[,-1]
lfsr.bma=read.table("../../../Dropbox/simulationsmay12/independentsim_all/noashindependentwithzerobmaalllfsr.txt")[,-1]
lfsr.ash=read.table("../../../Dropbox/simulationsmay12/independentsim_all/univariate.ashind.lfsr.txt")




standard.error=data$sebetahat

pm.mash.beta=mash.means*standard.error
pm.bma.beta=bma.means*standard.error
pm.ash.beta=ash.means*standard.error

thresh=0.05

Here, we show the Proportion of Sharing by Sign:

sigmat=(lfsr.mash<=thresh)
nsig= rowSums(sigmat)
(signall=mean(het.norm(pm.mash.beta[nsig>0,])>0.5))

## [1] 0.1285795

(signall.mash=mean(het.norm(pm.mash.beta[1:400,])>0.5))

## [1] 0.1285795

##BMA
sigmat=(lfsr.bma<=thresh)
nsig= rowSums(sigmat)
(signall=mean(het.norm(pm.bma.beta[nsig>0,])>0.5))

## [1] 0.1288068

(signall.bma=mean(het.norm(pm.bma.beta[1:400,])>0.5))

## [1] 0.1288068

##ASH
sigmat=(lfsr.ash<thresh)
nsig= rowSums(sigmat)
(signall=mean(het.norm(pm.ash.beta[nsig>0,])>0.5))

## [1] 0.1233225

(signall.ash=mean(het.norm(pm.ash.beta[1:400,])>0.5))

## [1] 0.1283523

####SHow that results are robust in specific analysis

(truth=(mean(het.norm(data$beta[1:400,])>0.5)))

## [1] 0.1277841

(standard=mean(het.norm(data$betahat[1:400,])>0.5))

## [1] 0.1286932

(RMLE=sqrt(mean((standard-truth)^2)))

## [1] 0.0009090909

(RRMSE.mash=sqrt(mean((signall.mash-truth)^2))/RMLE)

## [1] 0.875

(RRMSE.bma=sqrt(mean((signall.bma-truth)^2))/RMLE)

## [1] 1.125

(RRMSE.ash=sqrt(mean((signall.ash-truth)^2))/RMLE)

## [1] 0.625

rmse.all.table=cbind(mash=RRMSE.mash,bmalite=RRMSE.bma,ash=RRMSE.ash)
barplot(as.numeric(rmse.all.table),main="Shared, Untructured Effects: Sign heterogeneity",
        ylab="relative error (RRMSE)",xlab="Method",col=c("green","blue","red"),names=colnames(rmse.all.table),ylim=c(0,1.5),cex.main=1.5,cex.lab=1,cex.names=1,las=2)