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 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, ...)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. Two exceptions here are:
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.
# 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)
#> # A tibble: 3 × 8
#> term atmean estimate std.error statistic p.value conf.low conf.high
#> <chr> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl TRUE 0.0538 0.113 0.475 0.635 -0.178 0.286
#> 2 hp TRUE 0.00359 0.00290 1.24 0.216 -0.00236 0.00954
#> 3 wt TRUE -1.01 0.668 -1.51 0.131 -2.38 0.359
# compare with the naive model coefficients of the same logit call
tidy(
glm(am ~ cyl + hp + wt, family = binomial, data = mtcars),
conf.int = TRUE
)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> # A tibble: 4 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 19.7 8.12 2.43 0.0152 8.56 44.3
#> 2 cyl 0.488 1.07 0.455 0.649 -1.53 3.12
#> 3 hp 0.0326 0.0189 1.73 0.0840 0.00332 0.0884
#> 4 wt -9.15 4.15 -2.20 0.0276 -21.4 -3.48
augment(mod_logmfx)
#> # A tibble: 32 × 11
#> .rownames am cyl hp wt .fitted .resid .hat .sigma .cooksd
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 1 6 110 2.62 2.24 0.449 0.278 0.595 1.42e-2
#> 2 Mazda RX4 Wag 1 6 110 2.88 -0.0912 1.22 0.352 0.529 2.30e-1
#> 3 Datsun 710 1 4 93 2.32 3.46 0.249 0.0960 0.602 9.26e-4
#> 4 Hornet 4 Drive 0 6 110 3.22 -3.20 -0.282 0.0945 0.601 1.17e-3
#> 5 Hornet Sportab… 0 8 175 3.44 -2.17 -0.466 0.220 0.595 1.03e-2
#> 6 Valiant 0 6 105 3.46 -5.61 -0.0856 0.0221 0.604 2.12e-5
#> 7 Duster 360 0 8 245 3.57 -1.07 -0.766 0.337 0.576 6.55e-2
#> 8 Merc 240D 0 4 62 3.19 -5.51 -0.0897 0.0376 0.603 4.10e-5
#> 9 Merc 230 0 4 95 3.15 -4.07 -0.184 0.122 0.603 6.76e-4
#> 10 Merc 280 0 6 123 3.44 -4.84 -0.126 0.0375 0.603 8.02e-5
#> # ℹ 22 more rows
#> # ℹ 1 more variable: .std.resid <dbl>
glance(mod_logmfx)
#> # A tibble: 1 × 8
#> null.deviance df.null logLik AIC BIC deviance df.residual nobs
#> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 43.2 31 -4.92 17.8 23.7 9.84 28 32
# another example, this time using probit regression
mod_probmfx <- probitmfx(am ~ cyl + hp + wt, atmean = TRUE, data = mtcars)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
tidy(mod_probmfx, conf.int = TRUE)
#> # A tibble: 3 × 8
#> term atmean estimate std.error statistic p.value conf.low conf.high
#> <chr> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl TRUE 0.0616 0.112 0.548 0.583 -0.169 0.292
#> 2 hp TRUE 0.00383 0.00282 1.36 0.174 -0.00194 0.00960
#> 3 wt TRUE -1.06 0.594 -1.78 0.0753 -2.27 0.160
augment(mod_probmfx)
#> # A tibble: 32 × 11
#> .rownames am cyl hp wt .fitted .resid .hat .sigma .cooksd
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 1 6 110 2.62 1.21 0.490 0.308 0.585 2.05e-2
#> 2 Mazda RX4 Wag 1 6 110 2.88 -0.129 1.27 0.249 0.526 1.36e-1
#> 3 Datsun 710 1 4 93 2.32 1.85 0.256 0.134 0.594 1.48e-3
#> 4 Hornet 4 Drive 0 6 110 3.22 -1.92 -0.237 0.116 0.594 1.05e-3
#> 5 Hornet Sportab… 0 8 175 3.44 -1.25 -0.474 0.236 0.587 1.20e-2
#> 6 Valiant 0 6 105 3.46 -3.30 -0.0312 0.0111 0.596 1.39e-6
#> 7 Duster 360 0 8 245 3.57 -0.595 -0.804 0.285 0.567 5.32e-2
#> 8 Merc 240D 0 4 62 3.19 -3.31 -0.0304 0.0179 0.596 2.15e-6
#> 9 Merc 230 0 4 95 3.15 -2.47 -0.116 0.130 0.596 2.89e-4
#> 10 Merc 280 0 6 123 3.44 -2.85 -0.0662 0.0315 0.596 1.84e-5
#> # ℹ 22 more rows
#> # ℹ 1 more variable: .std.resid <dbl>
glance(mod_probmfx)
#> # A tibble: 1 × 8
#> null.deviance df.null logLik AIC BIC deviance df.residual nobs
#> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 43.2 31 -4.80 17.6 23.5 9.59 28 32