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 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`

. Two exceptions here are:

## 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

Other lm tidiers:
`augment.glm()`

,
`augment.lm()`

,
`glance.glm()`

,
`glance.lm()`

,
`glance.summary.lm()`

,
`glance.svyglm()`

,
`tidy.glm()`

,
`tidy.lm()`

,
`tidy.mlm()`

,
`tidy.summary.lm()`

## 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(lm.beta)
# fit models
mod <- stats::lm(speed ~ ., data = cars)
std <- lm.beta(mod)
# summarize model fit with tidiers
tidy(std, conf.int = TRUE)
#> # A tibble: 2 × 8
#> term estimate std_estimate std.error statistic p.value conf.low
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 8.28 NA 0.874 9.47 1.44e-12 NA
#> 2 dist 0.166 0.807 0.0175 9.46 1.49e-12 0.772
#> # ℹ 1 more variable: conf.high <dbl>
# generate data
ctl <- c(4.17, 5.58, 5.18, 6.11, 4.50, 4.61, 5.17, 4.53, 5.33, 5.14)
trt <- c(4.81, 4.17, 4.41, 3.59, 5.87, 3.83, 6.03, 4.89, 4.32, 4.69)
group <- gl(2, 10, 20, labels = c("Ctl", "Trt"))
weight <- c(ctl, trt)
# fit models
mod2 <- lm(weight ~ group)
std2 <- lm.beta(mod2)
# summarize model fit with tidiers
tidy(std2, conf.int = TRUE)
#> # A tibble: 2 × 8
#> term estimate std_estimate std.error statistic p.value conf.low
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 5.03 NA 0.220 22.9 9.55e-15 NA
#> 2 groupTrt -0.371 -0.270 0.311 -1.19 2.49e- 1 -0.925
#> # ℹ 1 more variable: conf.high <dbl>
```