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 class 'betamfx'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
```

## Arguments

- x
A

`betamfx`

object.- 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

The `mfx`

package provides methods for calculating marginal effects
for various generalized linear models (GLMs). Unlike standard linear
models, estimated model coefficients in a GLM cannot be directly
interpreted as marginal effects (i.e., the change in the response variable
predicted after a one unit change in one of the regressors). This is
because the estimated coefficients are multiplicative, dependent on both
the link function that was used for the estimation and any other variables
that were included in the model. When calculating marginal effects, users
must typically choose whether they want to use i) the average observation
in the data, or ii) the average of the sample marginal effects. See
`vignette("mfxarticle")`

from the `mfx`

package for more details.

## See also

`tidy.betareg()`

, `mfx::betamfx()`

Other mfx tidiers:
`augment.betamfx()`

,
`augment.mfx()`

,
`glance.betamfx()`

,
`glance.mfx()`

,
`tidy.mfx()`

## 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.

- atmean
TRUE if the marginal effects were originally calculated as the partial effects for the average observation. If FALSE, then these were instead calculated as average partial effects.

## Examples

```
library(mfx)
# Simulate some data
set.seed(12345)
n <- 1000
x <- rnorm(n)
# Beta outcome
y <- rbeta(n, shape1 = plogis(1 + 0.5 * x), shape2 = (abs(0.2 * x)))
# Use Smithson and Verkuilen correction
y <- (y * (n - 1) + 0.5) / n
d <- data.frame(y, x)
mod_betamfx <- betamfx(y ~ x | x, data = d)
tidy(mod_betamfx, conf.int = TRUE)
#> # A tibble: 1 × 8
#> term atmean estimate std.error statistic p.value conf.low conf.high
#> <chr> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 x TRUE 0.0226 0.00801 2.82 0.00483 0.00686 0.0383
# Compare with the naive model coefficients of the equivalent betareg call (not run)
# tidy(betamfx(y ~ x | x, data = d), conf.int = TRUE)
augment(mod_betamfx)
#> # A tibble: 1,000 × 4
#> y x .fitted .cooksd
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.951 0.586 0.809 0.000189
#> 2 0.714 0.709 0.811 0.0000993
#> 3 0.999 -0.109 0.793 0.000273
#> 4 0.998 -0.453 0.785 0.000334
#> 5 0.999 0.606 0.809 0.000342
#> 6 0.562 -1.82 0.751 0.000878
#> 7 0.999 0.630 0.810 0.000348
#> 8 0.999 -0.276 0.789 0.000294
#> 9 0.744 -0.284 0.789 0.0000134
#> 10 0.999 -0.919 0.774 0.000551
#> # ℹ 990 more rows
glance(mod_betamfx)
#> # A tibble: 1 × 7
#> pseudo.r.squared df.null logLik AIC BIC df.residual nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 0.00726 998 1897. -3787. -3767. 996 1000
```