# NOT RUN {
R <- 160
wgtnames <- paste("repwgt", seq(0,R,by=1), sep="")
mwgtname=wgtnames[1]
repwgtnames=wgtnames[2:(R+1)]
fayfactor=0.5
model2 <- ' # outcome
numcg ~ u0*1 + c*workban + b1*sp_adltban + b2*sp_kidsban
# mediator
sp_adltban ~ u1*1 + a1*workban
sp_kidsban ~ u2*1 + a2*workban
#covariance of residuals
sp_adltban ~~ sp_kidsban
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
fit.BRR2 <- med.fit.BRR(model=model2, data=MedData, mwgtname=mwgtname,
repwgtnames=repwgtnames, fayfactor, parallel='parallel', ncore=4)
temp <- med.p.adjust(fit=fit.BRR2, med.eff=c('a1b1' , 'a2b2'))
#
# Adjustment for multi mediation tests:
#
# Effect p Value adj.p Value
# a1b1 0.003667674 0.007335347
# a2b2 0.217228711 0.217228711
#
# NOTE: p Value adjustment method is holm
#
#########################################
# To catch the unformatted results:
temp
#
# $med.eff
# [1] "a1b1" "a2b2"
#
# $org.p.value
# [1] 0.003667674 0.217228711
#
# $adj.p.value
# [1] 0.007335347 0.217228711
# }
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