mediate_tsls: Two-stage Least Squares Estimation of the Average Causal...
In mediation: Causal Mediation Analysis

Description Usage Arguments Value References Examples

Estimate quantities for causal mediation analysis using an instrumental variable estimator.

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mediate_tsls(model.m, model.y, treat = "treat.name", conf.level = 0.95,
  robustSE = FALSE, cluster = NULL, boot = FALSE, sims = 1000,
  est_se = TRUE, ...)

`model.m`	a fitted model object for mediator, of class `lm`.
`model.y`	a fitted model object for outcome, of class `lm`.
`treat`	a character string indicating the name of the treatment variable used in the models.
`conf.level`	level of the returned two-sided confidence intervals. Default is to return the 2.5 and 97.5 percentiles of the simulated quantities.
`robustSE`	a logical value. If 'TRUE', heteroskedasticity-consistent standard errors will be used. Default is 'FALSE'.
`cluster`	a variable indicating clusters for standard errors. Note that this should be a vector of cluster indicators itself, not a character string for the name of the variable.
`boot`	a logical value. if `FALSE` analytic confidence intervals based on Aroian (1947) will be used; if `TRUE` nonparametric bootstrap will be used. Default is `FALSE`.
`sims`	number of Monte Carlo draws for nonparametric bootstrap.
`est_se`	estimate standard errors. Primarily for internal use. Default is `TRUE`.
`...`	other arguments passed to vcovCL in the sandwich package: typically the `type` argument for heteroskedasticity-consistent standard errors.

mediate returns an object of class "mediate", "mediate.tsls", a list that contains the components listed below.

The function summary can be used to obtain a table of the results.

`d1`	point estimate for average causal mediation effects.
`d1.ci`	confidence intervals for average causal mediation effect. The confidence level is set at the value specified in 'conf.level'.
`z0`	point estimates for average direct effect.
`z0.ci`	confidence intervals for average direct effect.
`z0.p`	two-sided p-values for average causal direct effect.
`n0`	the "proportions mediated", or the size of the average causal mediation effect relative to the total effect.
`n0.ci`	confidence intervals for the proportion mediated.
`n0.p`	two-sided p-values for proportion mediated.
`tau.coef`	point estimate for total effect.
`tau.ci`	confidence interval for total effect.
`tau.p`	two-sided p-values for total effect.
`boot`	logical, the `boot` argument used.
`treat`	a character string indicating the name of the 'treat' variable used.
`mediator`	a character string indicating the name of the 'mediator' variable used.
`INT`	a logical value indicating whether the model specification allows the effects to differ between the treatment and control conditions.
`conf.level`	the confidence level used.
`model.y`	the outcome model used.
`model.m`	the mediator model used.
`nobs`	number of observations in the model frame for 'model.m' and 'model.y'. May differ from the numbers in the original models input to 'mediate' if 'dropobs' was 'TRUE'.
`cluster`	the clusters used.

Aroian, L. A. 1947. The probability function of the product of two normally distributed variables. *Annals of Mathematical Statistics,* 18, 265-271.

# Generate data. We use TSLS to address unobserved confounding (n).
set.seed(123)
sims <- 1000
dat <- data.frame(z = sample(0:1, sims, replace = TRUE), 
                  t = sample(0:1, sims, replace = TRUE))
dat$n <- rnorm(sims, mean = 1)
dat$m <- rnorm(sims, mean = dat$z * 0.3 + dat$t * 0.2 + dat$n * 0.7, sd = 0.2)
dat$y <- rnorm(sims, mean = 5 + dat$t + dat$m * (-3) + dat$n, sd = 1)
model.m <- lm(m ~ t + z, data = dat)
model.y <- lm(y ~ t + m, data = dat)
cluster <- factor(sample(1:3, sims, replace = TRUE))
med <- mediate_tsls(model.m, model.y, cluster = cluster, treat = "t")
summary(med)