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, ...)
x | A |
---|---|
conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level | The confidence level to use for the confidence interval
if |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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.
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
#>#> #> #>#>#> #> #> #> #> #> #>#>#>#> #>#>#> #>data(jobs) 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") tidy(mod)#> # 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