tidy.mediate: Tidy a(n) mediate object

Description

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

# S3 method for mediate
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Value

A tibble::tibble() with columns:

conf.high: Upper bound on the confidence interval for the estimate.
conf.low: Lower bound on the confidence interval for the estimate.
estimate: The estimated value of the regression term.
p.value: The two-sided p-value associated with the observed statistic.
statistic: The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
std.error: The standard error of the regression term.
term: The name of the regression term.

Arguments

x

A mediate object produced by a call to mediation::mediate().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

Details

The tibble has four rows. The first two indicate the mediated effect in the control and treatment groups, respectively. And the last two the direct effect in each group.

Examples

Run this code

if (FALSE) { # rlang::is_installed("mediation")

# load libraries for models and data
library(mediation)

data(jobs)

# fit models
b <- lm(job_seek ~ treat + econ_hard + sex + age, data = jobs)
c <- lm(depress2 ~ treat + job_seek + econ_hard + sex + age, data = jobs)
mod <- mediate(b, c, sims = 50, treat = "treat", mediator = "job_seek")

# summarize model fit with tidiers
tidy(mod)
tidy(mod, conf.int = TRUE)
tidy(mod, conf.int = TRUE, conf.level = .99)
}