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 speedglm
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

## Arguments

x |
A `speedglm` object returned from `speedglm::speedglm()` . |

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. |

exponentiate |
Logical indicating whether or not to exponentiate the
the coefficient estimates. This is typical for logistic and multinomial
regressions, but a bad idea if there is no log or logit link. Defaults
to `FALSE` . |

... |
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. |

## 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 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 5.50 0.190 28.9 0.0000000152
#> 2 log(u) -0.602 0.0553 -10.9 0.0000122

#> # A tibble: 1 x 8
#> null.deviance df.null logLik AIC BIC deviance df.residual nobs
#> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 3.51 8 -26.2 58.5 59.1 0.163 7 9