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 mle2
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
```

## Arguments

- x
An

`mle2`

object created by a call to`bbmle::mle2()`

.- 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:

## 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(bbmle)
#> Loading required package: stats4
#>
#> Attaching package: ‘bbmle’
#> The following object is masked from ‘package:dfidx’:
#>
#> slice
#> The following object is masked from ‘package:ordinal’:
#>
#> slice
#> The following object is masked from ‘package:dplyr’:
#>
#> slice
# generate data
x <- 0:10
y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
d <- data.frame(x, y)
# fit model
fit <- mle2(y ~ dpois(lambda = ymean),
start = list(ymean = mean(y)), data = d
)
# summarize model fit with tidiers
tidy(fit)
#> # A tibble: 1 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 ymean 11.5 1.02 11.3 1.86e-29
```