mdn: Mediation Analysis via Likelihood
In iMediate: Likelihood Methods for Mediation Analysis

Description Usage Arguments Details Value Author(s) References Examples

View source: R/mdn.R

mdn conducts mediation analysis in terms of likelihood.

1	mdn(fit.M, fit.Y, X, sig.level = 0.05, B = 0)

`fit.M`	a fitted model object for mediator. It is an object from which the function `logLik` can extract the log-likelihood. Examples include those from “`lm`”, “`glm`”, etc.
`fit.Y`	a fitted model object for outcome. It can be of a class different from the model for the mediator
`X`	a character string of the name of the treatment variable.
`sig.level`	a numerical variable specifying the significance level for the test of the mediated effect.
`B`	an integer specifying the number of replicates used in the bootstrapping method for the confidence interval. Default value is 0 and bootstrapping is not conducted

Necessary log-likelihoods are extracted from the two fitted models. Various effects are then calculated. Significance of the mediated effect is known up to whether it is larger or smaller than sig.level. If it is larger, a 1 is reported; otherwise a 0 is reported. There is no p-value.

A list with class “mdn” containing the following components:

`result`	a data frame containing the results of the mediation analysis. The are five variables. They include estimates of various effects and lower and upper bounds of the boostrap confidence interval at level (1-`sig.level`) followed by test statistics and their respective p-values.
`test`	a character string specifying the test statistic used for the mediated effect
`Test`	a numerical value of 0 or 1. If the specified test statistic is significant, its value is 1; otherwise its value is 0
`sig.level`	a numerical variable specifying the significance level for the test of the mediated effect.
`Sample.size`	number of subjects in the data
`B`	an integer specifying the number of replicates used for the bootstrapping

Kai Wang <kai-wang@uiowa.edu>

Berger, R.L. (1997) Likelihood ratio tests and intersection-union tests. Advances in statistical decision theory and applications. Birkh\"auser Boston, 225-237.

Wang, K. (2019) Likelihood-based analysis of the statistical effects of a treatment on an outcome. Submitted.

data("jobs", package = "mediation")

fit.M <- lm(job_seek ~ treat + econ_hard + sex + age, data=jobs)
fit.Y <- lm(depress2 ~ treat + job_seek + econ_hard + sex + age, data=jobs)
mdn(fit.M, fit.Y, "treat")
mdn(fit.M, fit.Y, "treat", B=100)