Lüdecke D (2018). ggeffects: Tidy Data Frames of Marginal Effects from Regression Models. Journal of Open Source Software, 3(26), 772. doi: 10.21105/joss.00772
Results of regression models are typically presented as tables that are easy to understand. For more complex models that include interaction or quadratic / spline terms, tables with numbers are less helpful and difficult to interpret. In such cases, marginal effects or adjusted predictions are far easier to understand. In particular, the visualization of such effects or predictions allows to intuitively get the idea of how predictors and outcome are associated, even for complex models.
ggeffects is a lightweight package that aims at easily calculating marginal effects and adjusted predictions (or: estimated marginal means) at the mean or at representative values of covariates (see definitions here) from statistical models, i.e. predictions generated by a model when one holds the nonfocal variables constant and varies the focal variable(s). Furthermore, it is possible to compute contrasts or pairwise comparisons, to test predictions and differences in predictions for statistical significance.
This is achieved by three core ideas that describe the philosophy of the function design:
1) Functions are typesafe and always return a data frame with the same, consistent structure;
2) there is a simple, unique approach to calculate marginal effects/adjusted predictions and estimated marginal means for many different models;
3) the package supports “labelled data” (Lüdecke 2018), which allows human readable annotations for graphical outputs.
This means, users do not need to care about any expensive steps after
modeling to visualize the results. The returned as data frame is ready
to use with the ggplot2package, however, there is also a
plot()
method to easily create publicationready figures.
 Type  Source  Command 

 Release  CRAN  install.packages("ggeffects")

 Development  r  universe  install.packages("ggeffects", repos = "https://strengejacke.runiverse.dev")

 Development  GitHub  remotes::install_github("strengejacke/ggeffects")

Or you can run
ggeffects::install_latest()
to install the latest development version from runiverse.
There is no common language across fields regarding a unique meaning of
“marginal effects”. Thus, the wording throughout this package may vary.
Maybe “adjusted predictions” comes closest to what ggeffects
actually does. To avoid confusion about what is actually calculated and
returned by the package’s functions ggpredict()
, ggemmeans()
and
ggeffect()
, it is recommended to read this
vignette
about the different terminology and its meanings.
Please visit https://strengejacke.github.io/ggeffects/ for documentation and vignettes. For questions about the functionality, you may either contact me via email or also file an issue.
Marginal effects and adjusted predictions can be calculated for many
different models. Currently supported modelobjects are: averaging
,
bamlss
, bayesx
, betabin
, betareg
, bglmer
, blmer
, bracl
,
brglm
, brmsfit
, brmultinom
, cgam
, cgamm
, clm
, clm2
,
clmm
, coxph
, fixest
, gam
(package mgcv), Gam
(package
gam), gamlss
, gamm
, gamm4
, gee
, geeglm
, glm
, glm.nb
,
glmer
, glmer.nb
, glmmTMB
, glmmPQL
, glmrob
, glmRob
, glmx
,
gls
, hurdle
, ivreg
, lm
, lm_robust
, lme
, lmer
, lmrob
,
lmRob
, logistf
, logitr
, lrm
, mclogit
, mlogit
, MixMod
,
MCMCglmm
, mixor
, multinom
, negbin
, nlmer
, ols
, orm
, plm
,
polr
, rlm
, rlmer
, rq
, rqss
, stanreg
, survreg
, svyglm
,
svyglm.nb
, tidymodels
, tobit
, truncreg
, vgam
, wbm
,
zeroinfl
and zerotrunc
.
Support for models varies by function, i.e. although ggpredict()
,
ggemmeans()
and ggeffect()
support most models, some models are only
supported exclusively by one of the three functions. Other models not
listed here might work as well, but are currently not tested.
Interaction terms, splines and polynomial terms are also supported. The
main functions are ggpredict()
, ggemmeans()
and ggeffect()
. There
is a generic plot()
method to plot the results using ggplot2.
The returned data frames always have the same, consistent structure and
column names, so it’s easy to create ggplotplots without the need to
rewrite the function call. x
and predicted
are the values for the
x and yaxis. conf.low
and conf.high
could be used as ymin
and
ymax
aesthetics for ribbons to add confidence bands to the plot.
group
can be used as groupingaesthetics, or for faceting.
ggpredict()
requires at least one, but not more than four terms
specified in the terms
argument. Predicted values of the response,
along the values of the first term are calculated, optionally grouped by
the other terms specified in terms
.
library(ggeffects)
library(splines)
data(efc)
fit < lm(barthtot ~ c12hour + bs(neg_c_7) * c161sex + e42dep, data = efc)
ggpredict(fit, terms = "c12hour")
#> # Predicted values of barthtot
#>
#> c12hour  Predicted  95% CI
#> 
#> 4  67.89  [65.81, 69.96]
#> 12  67.07  [65.10, 69.05]
#> 22  66.06  [64.18, 67.94]
#> 36  64.64  [62.84, 66.45]
#> 49  63.32  [61.51, 65.14]
#> 70  61.20  [59.22, 63.17]
#> 100  58.15  [55.70, 60.60]
#> 168  51.26  [47.27, 55.26]
#>
#> Adjusted for:
#> * neg_c_7 = 11.83
#> * c161sex = 1.76
#> * e42dep = 2.93
A possible call to ggplot could look like this:
library(ggplot2)
mydf < ggpredict(fit, terms = "c12hour")
ggplot(mydf, aes(x, predicted)) +
geom_line() +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .1)
However, there is also a plot()
method. This method uses convenient
defaults, to easily create the most suitable plot for the marginal
effects.
mydf < ggpredict(fit, terms = "c12hour")
plot(mydf)
With three variables, predictions can be grouped and faceted.
ggpredict(fit, terms = c("neg_c_7", "c161sex", "e42dep"))
#> # Predicted values of barthtot
#>
#> # c161sex = 1
#> # e42dep = 1
#>
#> neg_c_7  Predicted  95% CI
#> 
#> 7  102.74  [95.96, 109.52]
#> 12  102.27  [97.10, 107.44]
#> 17  93.79  [86.95, 100.64]
#> 28  164.57  [95.88, 233.27]
#>
#> # c161sex = 2
#> # e42dep = 1
#>
#> neg_c_7  Predicted  95% CI
#> 
#> 7  109.54  [105.19, 113.88]
#> 12  99.81  [ 95.94, 103.68]
#> 17  94.90  [ 90.20, 99.60]
#> 28  90.26  [ 71.77, 108.76]
#>
#> # c161sex = 1
#> # e42dep = 2
#>
#> neg_c_7  Predicted  95% CI
#> 
#> 7  83.73  [77.31, 90.15]
#> 12  83.26  [78.94, 87.59]
#> 17  74.79  [68.67, 80.90]
#> 28  145.57  [76.90, 214.23]
#>
#> # c161sex = 2
#> # e42dep = 2
#>
#> neg_c_7  Predicted  95% CI
#> 
#> 7  90.53  [86.71, 94.35]
#> 12  80.80  [78.16, 83.44]
#> 17  75.90  [72.28, 79.51]
#> 28  71.26  [53.04, 89.47]
#>
#> # c161sex = 1
#> # e42dep = 3
#>
#> neg_c_7  Predicted  95% CI
#> 
#> 7  64.72  [58.27, 71.17]
#> 12  64.26  [60.29, 68.22]
#> 17  55.78  [50.03, 61.53]
#> 28  126.56  [57.88, 195.24]
#>
#> # c161sex = 2
#> # e42dep = 3
#>
#> neg_c_7  Predicted  95% CI
#> 
#> 7  71.52  [67.59, 75.46]
#> 12  61.79  [59.78, 63.80]
#> 17  56.89  [53.86, 59.92]
#> 28  52.25  [34.18, 70.32]
#>
#> # c161sex = 1
#> # e42dep = 4
#>
#> neg_c_7  Predicted  95% CI
#> 
#> 7  45.72  [38.85, 52.58]
#> 12  45.25  [41.02, 49.48]
#> 17  36.77  [30.96, 42.59]
#> 28  107.55  [38.83, 176.28]
#>
#> # c161sex = 2
#> # e42dep = 4
#>
#> neg_c_7  Predicted  95% CI
#> 
#> 7  52.51  [47.88, 57.15]
#> 12  42.79  [40.29, 45.29]
#> 17  37.88  [34.66, 41.11]
#> 28  33.24  [15.18, 51.31]
#>
#> Adjusted for:
#> * c12hour = 42.10
mydf < ggpredict(fit, terms = c("neg_c_7", "c161sex", "e42dep"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
geom_line() +
facet_wrap(~facet)
plot()
works for this case, as well:
plot(mydf)
Next, an example of an interaction term. We want to know whether the two slopes are significantly different from each other.
fit < lm(neg_c_7 ~ c12hour + barthtot * c161sex + e42dep, data = efc)
result < ggpredict(fit, c("barthtot", "c161sex"))
plot(result)
This can be achieved by hypothesis_test()
.
hypothesis_test(result)
#> # Linear trend for barthtot
#>
#> c161sex  Contrast  95% CI  p
#> 
#> 12  7.09e03  [0.01, 0.03]  0.464
We can conclude that slopes (or “linear trends”) of barthtot
for the
different groups of c161sex
are not statistically significantly
different from each other.
More features are explained in detail in the packagevignettes.
In case you want / have to cite my package, please use
citation('ggeffects')
for citation information:
Lüdecke D (2018). ggeffects: Tidy Data Frames of Marginal Effects from Regression Models. Journal of Open Source Software, 3(26), 772. doi: 10.21105/joss.00772
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