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, ...)
```

## 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:

## 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.

## 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.

## Examples

```
# load libraries for models and data
library(mediation)
#> mediation: Causal Mediation Analysis
#> Version: 4.5.0
#>
#> Attaching package: ‘mediation’
#> The following object is masked from ‘package:psych’:
#>
#> mediate
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)
#> # A tibble: 4 × 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
tidy(mod, conf.int = TRUE)
#> # A tibble: 4 × 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
tidy(mod, conf.int = TRUE, conf.level = .99)
#> # A tibble: 4 × 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
```