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 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.level = 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

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

## 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: 4 x 8
#> response term estimate std.error statistic p.value conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg (Intercept) 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 (Intercept) -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.