Mediation analysis based on bootstrap
bmem(x, ram, indirect, v, method='tsml', ci='bc', cl=.95,
boot=1000, m=10, varphi=.1, st='i', robust=FALSE,
max_it=500, moment=FALSE, ...)The on-screen output includes the parameter estimates, bootstrap standard errors, and CIs.
A data set
RAM path for the mediaiton model
A vector of indirect effec
Indices of variables used in the mediation model. If omitted, all variables are used.
list: listwise deletion, pair: pairwise deletion, mi: multiple imputation, em: EM algorithm.
norm: normal approximation CI, perc: percentile CI, bc: bias-corrected CI, bca: BCa
Confidence level. Can be a vector.
Number of bootstraps
Number of imputations
Percent of data to be downweighted
Starting values
Robust method
Select mean structure or covariance analysis. moment=FALSE, covariance analysis. moment=TRUE, mean and covariance analysis.
Maximum number of iterations in EM
Other options for sem function can be used.
Zhiyong Zhang and Lijuan Wang
The indirect effect can be specified using equations such as a*b, a*b+c, and a*b*c+d*e+f. A vector of indirect effects can be used indirect=c('a*b', 'a*b+c').
Zhang, Z., & Wang, L. (2013). Methods for mediation analysis with missing data. Psychometrika, 78(1), 154-184.