Significance Test for Moderated Mediation
Function to test whether the average causal mediation effects and direct effects are significantly different between two moderator strata.
test.modmed(object, ...) ## S3 method for class 'mediate' test.modmed(object, covariates.1, covariates.2, sims = object$sims, conf.level = object$conf.level, ...) ## S3 method for class 'test.modmed.mediate' print(x, ...)
object |
output from |
... |
additional arguments. |
covariates.1 |
first set of value(s) of covariates (moderators) included
in the mediator and outcome models. See documentation for the
|
covariates.2 |
second set of value(s) of covariates (moderators) included in the mediator and outcome models. |
sims |
number of simulation draws the test will be based on. Defaults to the number used in the original mediate fit. |
conf.level |
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. |
x |
output from |
The function takes the original call to mediate and reruns
the algorithm twice with the two sets of covariates values. It
assumes that the objects in the environment in which the original mediate
call was made also exist in the current environment under the same variable
names, i.e., it evaluates the updated call in the current environment.
When applied to a mediate object, test.modmed returns
an object of class "test.modmed.mediate", a list composed of
"htest" objects. See t.test for more explanations of
htest objects. When applied to a mediate.order object, the
function returns an object of class "test.modmed.mediate.order"
which is a list composed of "htest.order" objects.
Teppei Yamamoto, Massachusetts Institute of Technology, teppei@mit.edu.
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.
# 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 interaction with moderator b.int <- lm(job_seek ~ treat*age + econ_hard + sex, data=jobs) d.int <- lm(depress2 ~ treat*job_seek*age + econ_hard + sex, data=jobs) # Initial mediate fit fit <- mediate(b.int, d.int, sims=50, treat="treat", mediator="job_seek") # Test for significance of moderated mediation test.modmed(fit, list(age = 20), list(age = 70), sims = 100)
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