Description Usage Arguments Value References Examples
This function is used to print the summary results of the mediation analysis with adjustment for multiplicity.
1 2 3 4 5 | med.summary(
fit = NULL,
med.eff = NULL,
p.adj.method = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr")
)
|
fit |
The model fit results of a mediation model. Note that it is a lavaan object. |
med.eff |
A vector of labels. The labels should be of the mediation effects in the estimated model. |
p.adj.method |
The method used to adjust for multiplicity ( |
A list including the effect labels, estimates, standard errors, p values, and adjusted p values if there are more than one mediation effects.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289–300. DOI:10.2307/2346101
Benjamini, Y., & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165–1188. DOI:10.1214/aos/1013699998
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70.
Hommel, G. (1988). A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika, 75, 383–386. DOI:10.1093/biomet/75.2.383
Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75, 800–803. DOI:10.1093/biomet/75.4.800
Mai, Y., Ha, T., & Soulakova, J. N. (2019). Multimediation Method With Balanced Repeated Replications For Analysis Of Complex Surveys. Structural Equation Modeling: A Multidisciplinary Journal. DOI:10.1080/10705511.2018.1559065
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of statistical software, 48(2), 1-36. DOI:10.18637/jss.v048.i02
Shaffer, J. P. (1995). Multiple hypothesis testing. Annual Review of Psychology, 46, 561–576.
Sarkar, S. (1998). Some probability inequalities for ordered MTP2 random variables: a proof of Simes conjecture. Annals of Statistics, 26, 494–504. DOI:10.1214/aos/1028144846
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | 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')
temp <- med.summary(fit=fit.BRR2, med.eff=c('a1b1' , 'a2b2'))
#
# MedSurvey 1.1.0
#
# Multimediation with Complex Survey Data:
#
# Effect Est. BRR SE. p Value adj.p Value
#
# a1b1 -0.017475544 0.006014820 0.003667674 0.007335347
# a2b2 -0.007244189 0.005870823 0.217228711 0.217228711
#
# NOTE:
# p Value adjustment method is holm
# Standard errors type is BRR SE.
#
#
######################################
# To catch the unformatted results:
temp
#
# $med.label
# [1] "a1b1" "a2b2"
#
# $med.est
# [1] -0.017475544 -0.007244189
#
# $med.se
# [1] 0.006014820 0.005870823
#
# $org.p.value
# [1] 0.003667674 0.217228711
#
# $adj.p.value
# [1] 0.007335347 0.217228711
#
# $se.type
# [1] "BRR SE."
#
# $p.adj.method
# [1] "holm"
#
|
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