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

## Arguments

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

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

The `Rchoice`

package provides "an implementation of simulated
maximum likelihood method for the estimation of Binary (Probit and Logit),
Ordered (Probit and Logit) and Poisson models with random parameters for
cross-sectional and longitudinal data."

## 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: 5 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 constant 6.01 4.64 1.30 0.195
#> 2 mpg -0.0957 0.134 -0.716 0.474
#> 3 hp -0.0244 0.0214 -1.14 0.253
#> 4 factor(cyl)6 -0.980 1.23 -0.797 0.426
#> 5 factor(cyl)8 -6.39 938. -0.00682 0.995

#> # A tibble: 1 × 5
#> logLik AIC BIC df nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -7.30 24.6 31.9 NA 32