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ZYmediate <- function(x, y, med, nboot = 2000, alpha = 0.05, kappa = 0.05, ...){
#
# Robust mediation analysis using M-estimator as
# described in Zu and Yuan, 2010, MBR, 45, 1--44.
#
# x[,1] is predictor
# x[,2] is mediator variable (m)
# y is outcome variable.
cl <- match.call()
SEED=FALSE
xout=FALSE
ep=0.00000001 # convergence criteria
B=nboot # the number of bootstrap replications
#kappa # the percent of cases to be controlled when robust method is used
# Zu and Yuan used .05, so this is the default value used here.
level=alpha # alpha level
Z=elimna(cbind(x,med,y))
p=3
n=nrow(Z)
HT=HuberTun(kappa,p)
r=HT$r
tau=HT$tau
H=robEst(Z,r,tau,ep)
R.v=H$u2*tau
oH=order(R.v)
oCaseH=(1:n)[oH] # case number with its Ri increases
oR.v=R.v[oH]
thetaH=H$theta
aH=thetaH[1]
bH=thetaH[2]
abH=aH*bH
muH=H$mu
SigmaH=H$Sigma
dH=H$d
### Use robust method
# point estimate
thetaH=H$theta
aH=thetaH[1]
bH=thetaH[2]
abH=aH*bH
muH=H$mu
SigmaH=H$Sigma
dH=H$d
# #Standard errors
# RH=SErob(Z,muH,SigmaH,thetaH,dH,r,tau)
#
# Zr=RH$Zr
# SEHI=RH$inf
# SEHS=RH$sand
#
# #Standard errors
# RH=SErob(Z,muH,SigmaH,thetaH,dH,r,tau)
#
# Zr=RH$Zr
# SEHI=RH$inf
# SEHS=RH$sand
#
#Standard errors
RH=SErob(Z,muH,SigmaH,thetaH,dH,r,tau)
Zr=RH$Zr
SEHI=RH$inf
SEHS=RH$sand
ParEstH<-round(cbind(thetaH,SEHI[1:6],SEHS[1:6]),3)
rnames<-c("a","b","c","vx","vem","vey")
ParEstH<-cbind(rnames,ParEstH)
res=t(ParEstH)
#
Res=BCI(Z,Zr,ab=3,abH,B,level)
result <- list(CI.ab=Res$CI,p.value=Res$pv,a.est=aH,b.est=bH,ab.est=abH, alpha = alpha, call = cl)
class(result) <- "robmed"
result
}
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