mediation (version 4.5.0)

test.modmed: Significance Test for Moderated Mediation

Description

Function to test whether the average causal mediation effects and direct effects are significantly different between two moderator strata.

Usage

test.modmed(object, ...)
# S3 method for mediate
test.modmed(object, covariates.1, covariates.2,
  sims = object$sims, conf.level = object$conf.level, ...)
# S3 method for test.modmed.mediate
print(x, ...)

Arguments

object

output from mediate function.

...

additional arguments.

covariates.1

first set of value(s) of covariates (moderators) included in the mediator and outcome models. See documentation for the covariates argument for the mediate function.

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 test.modmed function.

Value

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.

Details

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.

References

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.

See Also

mediate, test.TMint

Examples

# NOT RUN {
# 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)

# }