Function to test whether the average causal mediation effects and direct effects are significantly different between the treatment and control contitions.
level of the returned two-sided confidence intervals for the effect differences. By default it is set to the value used in the original mediate call.
test.TMint returns an object of class "
htest" when applied to a
mediate object. See
t.test for more explanations of the contents. The function returns an object of class "
htest.order" which has its own
Teppei Yamamoto, Massachusetts Institute of Technology, [email protected].
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.
Imai, K., Keele, L. and Tingley, D. (2010) A General Approach to Causal Mediation Analysis, Psychological Methods, Vol. 15, No. 4 (December), pp. 309-334.
Imai, K., Keele, L. and Yamamoto, T. (2010) Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects, Statistical Science, Vol. 25, No. 1 (February), pp. 51-71.
Imai, K., Keele, L., Tingley, D. and Yamamoto, T. (2009) "Causal Mediation Analysis Using R" in Advances in Social Science Research Using R, ed. H. D. Vinod New York: Springer.
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# Examples with JOBS II Field Experiment # **For illustration purposes a small number of simulations are used** data(jobs) # Fit mediator and outcome models allowing for treatment-mediator interaction b <- lm(job_seek ~ treat + econ_hard + sex + age, data=jobs) d <- lm(depress2 ~ treat*job_seek + econ_hard + sex + age, data=jobs) # Test for significance of interaction fit <- mediate(b, d, sims=50, treat="treat", mediator="job_seek") test.TMint(fit)
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