fimle.lnl: Full Information Maximum Likelihood Estimates in Linear...
In iMediate: Likelihood Methods for Mediation Analysis

Description Usage Arguments Details Value Author(s) References Examples

View source: R/fimle.lnl.R

Parameter estimates in system of correlated linear M-model and linear Y-model with treatment-mediator interaction using the full information maximum likelihood method.

1	fimle.lnl(fit.M, fit.Y, treatment, rho = 0, t0 = 0, t1 = 1, m = 1)

`fit.M`	a fitted model object for mediator. It must be an object generated by function “`lm`”
`fit.Y`	a fitted model object for outcome. It must be an object generated by function “`lm`”. It can contain treatment-mediator interaction
`treatment`	a character string of the name of the treatment variable. This variable takes numerical values
`rho`	a numerical variable specifying the correlation coefficient between the residual of the M-model and the residual of the Y-model. Its range is between `-1` and `1`
`t0`	a reference value for the treatment
`t1`	another value for the treatment
`m`	a value specifying the level of the mediator. Used for CDE computation between the residual of the M-model and the residual of the Y-model. Its range is between `-1` and `1`

P-values are computed from normal distribution.

A list containing the following components:

`M.model`	a data frame containing the results for the M-model
`Y.model`	a data frame containing the results for the Y-model
`Effects`	a data frame containing estimated ACME, ADE, Total Effect, and CDE for treatment values `t1` and `t0`
`Variance`	a matrix of variances and covariances of the parameters estimates

Kai Wang <kai-wang@uiowa.edu>

Wang, K. (2019) Maximum likelihood analysis of mediation models with treatment-mediator interaction. Revision 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)
fimle.lnl(fit.M, fit.Y, "treat", rho=0.2)

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)
fimle.lnl(fit.M, fit.Y, "treat", rho=0.5)