object of class 'mediate.mer' produced by 'mediate'.
treatment
a character string indicating the baseline treatment value of the estimated causal mediation effect and direct effect to plot. Can be either "control", "treated", or "both". If 'NULL' (default), both sets of estimates are plotted if and only if they differ.
group.plots
a logical value indicating whether group-specific effects should be plotted in addition to the population-averaged effects.
ask
a logical value. If 'TRUE', the user is asked for input before a new figure is plotted. Default is to ask only if the number of plots on current screen is fewer than necessary.
xlim
range of the horizontal axis.
ylim
range of the vertical axis.
xlab
label of the horizontal axis.
ylab
label of the vertical axis.
main
main title.
lwd
width of the horizontal bars for confidence intervals .
cex
size of the dots for point estimates.
col
color of the dots and horizontal bars for the estimates..
Tingley, D., Yamamoto, T., Hirose, K., Imai, K. and Keele, L. (2014). "mediation: R package for Causal Mediation Analysis", Journal of Statistical Software, Vol. 59, No. 5, pp. 1-38.
See Also
mediate, summary.mediate.mer.
Examples
# Examples with JOBS II Field Experiment
# **For illustration purposes a small number of simulations are used**
## Not run:
data(jobs)
require(lme4)
# educ: mediator group
# occp: outcome group
# Varying intercept for mediator
model.m <- glmer(job_dich ~ treat + econ_hard + (1 | educ),
family = binomial(link = "probit"), data = jobs)
# Varying intercept and slope for outcome
model.y <- glmer(work1 ~ treat + job_dich + econ_hard + (1 + treat | occp),
family = binomial(link = "probit"), data = jobs)
# Output based on mediator group
multilevel <- mediate(model.m, model.y, treat = "treat",
mediator = "job_dich", sims=50, group.out="educ")
#plot(multilevel, group.plots=TRUE)
## End(Not run)