bmem: Mediation analysis based on bootstrap

In bmemLavaan: Mediation Analysis with Missing Data and Non-Normal Data

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,  ...)

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.

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.

Value

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

Author(s)

Zhiyong Zhang, Shuigen Ming and Lijuan Wang

References

Zhang, Z., & Wang, L. (2013). Methods for mediation analysis with missing data. Psychometrika, 78(1), 154-184. doi: 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). doi: 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)

bmemLavaan documentation built on May 28, 2022, 5:06 p.m.