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.

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

## 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` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## 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.

## See also

## Value

A `tibble::tibble()`

with columns:

conf.highUpper bound on the confidence interval for the estimate.

conf.lowLower bound on the confidence interval for the estimate.

estimateThe estimated value of the regression term.

p.valueThe two-sided p-value associated with the observed statistic.

statisticThe value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.errorThe standard error of the regression term.

termThe name of the regression term.

## Examples

#> Registered S3 methods overwritten by 'Hmisc':
#> method from
#> summary.formula ergm
#> vcov.default fit.models

#> Registered S3 methods overwritten by 'lme4':
#> method from
#> cooks.distance.influence.merMod car
#> influence.merMod car
#> simulate.formula ergm
#> simulate.formula_lhs ergm
#> dfbeta.influence.merMod car
#> dfbetas.influence.merMod car

#> mediation: Causal Mediation Analysis
#> Version: 4.5.0

#>
#> Attaching package: ‘mediation’

#> The following object is masked from ‘package:psych’:
#>
#> mediate

#> # A tibble: 4 x 4
#> term estimate std.error p.value
#> <chr> <dbl> <dbl> <dbl>
#> 1 acme_0 -0.0143 0.0129 0.24
#> 2 acme_1 -0.0143 0.0129 0.24
#> 3 ade_0 -0.0315 0.0377 0.24
#> 4 ade_1 -0.0315 0.0377 0.24

#> # A tibble: 4 x 6
#> term estimate std.error p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 acme_0 -0.0143 0.0129 0.24 -0.0349 0.0103
#> 2 acme_1 -0.0143 0.0129 0.24 -0.0349 0.0103
#> 3 ade_0 -0.0315 0.0377 0.24 -0.105 0.0584
#> 4 ade_1 -0.0315 0.0377 0.24 -0.105 0.0584

#> # A tibble: 4 x 6
#> term estimate std.error p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 acme_0 -0.0143 0.0129 0.24 -0.0378 0.0243
#> 2 acme_1 -0.0143 0.0129 0.24 -0.0378 0.0243
#> 3 ade_0 -0.0315 0.0377 0.24 -0.106 0.0686
#> 4 ade_1 -0.0315 0.0377 0.24 -0.106 0.0686