tidy.mfx  R Documentation 
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 selfevident. 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 class 'mfx' tidy(x, conf.int = FALSE, conf.level = 0.95, ...) ## S3 method for class 'logitmfx' tidy(x, conf.int = FALSE, conf.level = 0.95, ...) ## S3 method for class 'negbinmfx' tidy(x, conf.int = FALSE, conf.level = 0.95, ...) ## S3 method for class 'poissonmfx' tidy(x, conf.int = FALSE, conf.level = 0.95, ...) ## S3 method for class '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. Cautionary note: Misspelled arguments will be
absorbed in

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.
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 twosided pvalue associated with the observed statistic. 
statistic 
The value of a Tstatistic to use in a hypothesis that the regression term is nonzero. 
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. 
tidy()
, mfx::logitmfx()
, mfx::negbinmfx()
, mfx::poissonmfx()
, mfx::probitmfx()
Other mfx tidiers:
augment.betamfx()
,
augment.mfx()
,
glance.betamfx()
,
glance.mfx()
,
tidy.betamfx()
# load libraries for models and data 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 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)
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