bmemLavaan (version 0.7)

bmem: Mediation analysis based on bootstrap

Description

Mediation analysis based on bootstrap

Usage

bmem(data, model, v, method='list', ci='perc', cl=.95, 
     boot=1000, m=10, varphi=.1, st='i', robust=FALSE, 
     max_it=500, parallel=FALSE, ncore=1,  ...)

Value

The on-screen output includes the parameter estimates, bootstrap standard errors, and CIs.

Arguments

data

A data set

model

RAM path for the mediaiton model

v

Indices of variables used in the mediation model. If omitted, all variables are used.

method

list: listwise deletion, pair: pairwise deletion, mi: multiple imputation, em: EM algorithm.

ci

norm: normal approximation CI, perc: percentile CI, bc: bias-corrected CI, bca: BCa

cl

Confidence level. Can be a vector.

boot

Number of bootstraps

m

Number of imputations

varphi

Percent of data to be downweighted in robust method

st

Starting values

robust

Whether to use roubst method

max_it

Maximum number of iterations in EM

parallel

Whether to use parallel method to calculate.

ncore

Number of cores for parallel method.

...

Other options for sem function can be used.

Author

Zhiyong Zhang, Shuigen Ming and Lijuan Wang

Details

The indirect effect can be specified using equations such as a*b, a*b+c, and a*b*c+d*e+f, which can be defined in 'model' parameter.

References

Zhang, Z., & Wang, L. (2013). Methods for mediation analysis with missing data. Psychometrika, 78(1), 154-184. tools:::Rd_expr_doi("https://doi.org/10.1007/s11336-012-9301-5")

Yuan, KH., Zhang, Z. Robust Structural Equation Modeling with Missing Data and Auxiliary Variables. Psychometrika 77, 803-826 (2012). tools:::Rd_expr_doi("https://doi.org/10.1007/s11336-012-9282-4")

Examples

data("PoliticalDemocracy")

model_l <- '
ind60 =~ x1 + g*x2 + h*x3
dem60 =~ y1 + d*y2 + e*y3 + f*y4
dem65 =~ y5 + d*y6 + e*y7 + f*y8

dem60 ~ a * ind60
dem65 ~ c * ind60 + b * dem60

y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
ind := a*b
'

fit_l <- bmem(data=PoliticalDemocracy, model = model_l, method='list', 
      ci='perc', boot=50, parallel = TRUE, ncore = 8)
summary(fit_l)