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 cross 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 lm.beta
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

## Arguments

x |
An `lm.beta` object created by lm.beta::lm.beta. |

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

If the linear model is an `mlm`

object (multiple linear model),
there is an additional column `response`

.

If you have missing values in your model data, you may need to refit
the model with `na.action = na.exclude`

.

## 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

#> # A tibble: 2 x 8
#> term estimate std_estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Interc… 8.28 0 0.874 9.47 1.44e-12 -1.76 1.76
#> 2 dist 0.166 0.807 0.0175 9.46 1.49e-12 0.772 0.842

#> # A tibble: 2 x 8
#> term estimate std_estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Interc… 5.03 0 0.220 22.9 9.55e-15 -0.463 0.463
#> 2 groupTrt -0.371 -0.270 0.311 -1.19 2.49e- 1 -0.925 0.384