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, ...)

x | A |
---|---|

conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |

conf.level | The confidence level to use for the confidence interval
if |

... | Additional arguments. Not used. Needed to match generic
signature only. |

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()`

A `tibble::tibble()`

with columns:

Upper bound on the confidence interval for the estimate.

Lower bound on the confidence interval for the estimate.

The estimated value of the regression term.

The two-sided p-value associated with the observed statistic.

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

The standard error of the regression term.

The name of the regression term.

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.

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) }