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.

The particular functions below provide generic tidy methods for objects returned by the mfx package, preserving the calculated marginal effects instead of the naive model coefficients. The returned tidy tibble will also include an additional "atmean" column indicating how the marginal effects were originally calculated (see Details below).

# S3 method for mfx
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for logitmfx
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for negbinmfx
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for poissonmfx
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for probitmfx
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

## Arguments

x A logitmfx, negbinmfx, poissonmfx, or probitmfx object. (Note that betamfx objects receive their own set of tidiers.) Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE. 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 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.

tidy(), mfx::logitmfx(), mfx::negbinmfx(), mfx::poissonmfx(), mfx::probitmfx()

Other mfx tidiers: augment.betamfx(), augment.mfx(), glance.betamfx(), glance.mfx(), tidy.betamfx()

## 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

if (FALSE) {
library(mfx)

## Get the marginal effects from a logit regression
mod_logmfx <- logitmfx(am ~ cyl + hp + wt, atmean = TRUE, data = mtcars)
tidy(mod_logmfx, conf.int = TRUE)

## Compare with the naive model coefficients of the same logit call (not run)
# tidy(glm(am ~ cyl + hp + wt, family = binomial, data = mtcars), conf.int = TRUE)

augment(mod_logmfx)
glance(mod_logmfx)

## Another example, this time using probit regression
mod_probmfx <- probitmfx(am ~ cyl + hp + wt, atmean = TRUE, data = mtcars)
tidy(mod_probmfx, conf.int = TRUE)
augment(mod_probmfx)
glance(mod_probmfx)
}