Description Usage Arguments Value References Examples

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

1 2 3 |

`model.m` |
a fitted model object for mediator, of class |

`model.y` |
a fitted model object for outcome, of class |

`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 |

`sims` |
number of Monte Carlo draws for nonparametric bootstrap. |

`est_se` |
estimate standard errors. Primarily for internal use. Default is |

`...` |
other arguments passed to vcovCL in the sandwich package:
typically the |

`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 |

`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.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# 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)
``` |

mediation documentation built on Oct. 9, 2019, 1:04 a.m.

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