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 mlm
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
```

## Arguments

- x
An

`mlm`

object created by`stats::lm()`

with a matrix as the response.- 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

In contrast to `lm`

object (simple linear model), tidy output for
`mlm`

(multiple linear model) objects contain 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.beta()`

,
`tidy.lm()`

,
`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

```
# fit model
mod <- lm(cbind(mpg, disp) ~ wt, mtcars)
# summarize model fit with tidiers
tidy(mod, conf.int = TRUE)
#> # A tibble: 4 × 8
#> response term estimate std.error statistic p.value conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg (Inte… 37.3 1.88 19.9 8.24e-19 33.5 41.1
#> 2 mpg wt -5.34 0.559 -9.56 1.29e-10 -6.49 -4.20
#> 3 disp (Inte… -131. 35.7 -3.67 9.33e- 4 -204. -58.2
#> 4 disp wt 112. 10.6 10.6 1.22e-11 90.8 134.
```