After fitting a model, it is useful generate model-based estimates (expected values, or adjusted predictions) of the response variable for different combinations of predictor values. Such estimates can be used to make inferences about relationships between variables.
The ggeffects package computes marginal means and adjusted predicted
values for the response, at the margin of specific values or levels from
certain model terms. The package is built around three core functions:
predict_response() (understanding results), test_predictions() (testing
results for statistically significant differences) and plot() (communicate
results).
By default, adjusted predictions or marginal means are by returned on the
response scale, which is the easiest and most intuitive scale to interpret
the results. There are other options for specific models as well, e.g. with
zero-inflation component (see documentation of the type-argument). The
result is returned as consistent data frame, which is nicely printed by
default. plot() can be used to easily create figures.
The main function to calculate marginal means and adjusted predictions is
predict_response(). In previous versions of ggeffects, the functions
ggpredict(), ggemmeans(), ggeffect() and ggaverage() were used to
calculate marginal means and adjusted predictions. These functions are still
available, but predict_response() as a "wrapper" around these functions is
the preferred way to do this now.
# S3 method for ggeffects
as.data.frame(
x,
row.names = NULL,
optional = FALSE,
...,
stringsAsFactors = FALSE,
terms_to_colnames = FALSE
)ggaverage(
model,
terms,
ci_level = 0.95,
type = "fixed",
typical = "mean",
condition = NULL,
back_transform = TRUE,
vcov = NULL,
vcov_args = NULL,
weights = NULL,
verbose = TRUE,
...
)
ggeffect(
model,
terms,
ci_level = 0.95,
bias_correction = FALSE,
verbose = TRUE,
...
)
ggemmeans(
model,
terms,
ci_level = 0.95,
type = "fixed",
typical = "mean",
condition = NULL,
interval = "confidence",
back_transform = TRUE,
vcov = NULL,
vcov_args = NULL,
bias_correction = FALSE,
weights = NULL,
verbose = TRUE,
...
)
ggpredict(
model,
terms,
ci_level = 0.95,
type = "fixed",
typical = "mean",
condition = NULL,
interval = "confidence",
back_transform = TRUE,
vcov = NULL,
vcov_args = NULL,
bias_correction = FALSE,
verbose = TRUE,
...
)
A data frame (with ggeffects class attribute) with consistent data columns:
"x": the values of the first term in terms, used as x-position in plots.
"predicted": the predicted values of the response, used as y-position in plots.
"std.error": the standard error of the predictions. Note that the standard
errors are always on the link-scale, and not back-transformed for non-Gaussian
models!
"conf.low": the lower bound of the confidence interval for the predicted values.
"conf.high": the upper bound of the confidence interval for the predicted values.
"group": the grouping level from the second term in terms, used as
grouping-aesthetics in plots.
"facet": the grouping level from the third term in terms, used to indicate
facets in plots.
The estimated marginal means (or predicted values) are always on the response scale!
For proportional odds logistic regression (see ?MASS::polr)
resp. cumulative link models (e.g., see ?ordinal::clm),
an additional column "response.level" is returned, which indicates
the grouping of predictions based on the level of the model's response.
Note that for convenience reasons, the columns for the intervals
are always named "conf.low" and "conf.high", even though
for Bayesian models credible or highest posterior density intervals
are returned.
There is an as.data.frame() method for objects of class ggeffects,
which has an terms_to_colnames argument, to use the term names as column
names instead of the standardized names "x" etc.
An object of class ggeffects, as returned by predict_response(),
ggpredict(), ggeffect(), ggaverage() or ggemmeans().
NULL or a character vector giving the row
names for the data frame. Missing values are not allowed.
logical. If TRUE, setting row names and
converting column names (to syntactic names: see
make.names) is optional. Note that all of R's
base package as.data.frame() methods use
optional only for column names treatment, basically with the
meaning of data.frame(*, check.names = !optional).
See also the make.names argument of the matrix method.
Arguments are passed down to ggpredict() (further down to predict())
or ggemmeans() (and thereby to emmeans::emmeans()), If type = "simulate",
... may also be used to set the number of simulation, e.g. nsim = 500.
When calling ggeffect(), further arguments passed down to effects::Effect().
logical: should the character vector be converted to a factor?
Logical, if TRUE, standardized column names (like
"x", "group" or "facet") are replaced by the variable names of the focal
predictors specified in terms.
A model object, or a list of model objects.
Names of those terms from model, for which predictions should
be displayed (so called focal terms). Can be:
A character vector, specifying the names of the focal terms. This is the
preferred and probably most flexible way to specify focal terms, e.g.
terms = "x [40:60]", to calculate predictions for the values 40 to 60.
A list, where each element is a named vector, specifying the focal terms
and their values. This is the "classical" R way to specify focal terms,
e.g. list(x = 40:60).
A formula, e.g. terms = ~ x + z, which is internally converted to a
character vector. This is probably the least flexible way, as you cannot
specify representative values for the focal terms.
A data frame representing a "data grid" or "reference grid". Predictions are then made for all combinations of the variables in the data frame.
terms at least requires one variable name. The maximum length is four terms,
where the second to fourth term indicate the groups, i.e. predictions of the first
term are grouped at meaningful values or levels of the remaining terms (see
values_at()). It is also possible to define specific values for focal
terms, at which adjusted predictions should be calculated (see details below).
All remaining covariates that are not specified in terms are "marginalized",
see the margin argument in ?predict_response. See also argument condition
to fix non-focal terms to specific values, and argument typical for
ggpredict() or ggemmeans().
Numeric, the level of the confidence intervals. Use
ci_level = NA if confidence intervals should not be calculated
(for instance, due to computation time). Typically, confidence intervals are
based on the returned standard errors for the predictions, assuming a t- or
normal distribution (based on the model and the available degrees of freedom,
i.e. roughly +/- 1.96 * SE). See introduction of
this vignette
for more details.
Character, indicating whether predictions should be conditioned
on specific model components or not, or whether population or unit-level
predictions are desired. Consequently, most options only apply for survival
models, mixed effects models and/or models with zero-inflation (and their
Bayesian counter-parts); only exception is type = "simulate", which is
available for some other model classes as well (which respond to
simulate()).
Note 1: For brmsfit-models with zero-inflation component, there is no
type = "zero_inflated" nor type = "zi_random"; predicted values for these
models always condition on the zero-inflation part of the model. The same
is true for MixMod-models from GLMMadaptive with zero-inflation
component (see 'Details').
Note 2: If margin = "empirical", or when calling ggaverage() respectively,
(i.e. counterfactual predictions), the type argument is handled differently.
It is set to "response" by default, but usually accepts all possible options
from the type-argument of the model's respective predict() method. E.g.,
passing a glm object would allow the options "response", "link", and
"terms". For models with zero-inflation component, the below mentioned
options "fixed", "zero_inflated" and "zi_prob" can also be used and will
be "translated" into the corresponding type option of the model's respective
predict()-method.
Note 3: If margin = "marginalmeans", or when calling ggemmeans()
respectively, type = "random" and type = "zi_random" are not available,
i.e. no unit-level predictions are possible.
"fixed" (or "count")
Predicted values are conditioned on the fixed effects or conditional
model only. For mixed models, predicted values are on the
population-level, i.e. re.form = NA when calling predict(). For
models with zero-inflation component, this type would return the
predicted mean from the count component (without conditioning on the
zero-inflation part).
"random"
This only applies to mixed models, and type = "random" does not
condition on the zero-inflation component of the model. Use this for
unit-level predictions, i.e. predicted values for each level of the
random effects groups. Add the name of the related random effect term
to the terms-argument (for more details, see this vignette).
"zero_inflated" (or "zi")
Predicted values are conditioned on the fixed effects and the
zero-inflation component, returning the expected value of the response
(mu*(1-p)). For For mixed models with zero-inflation component (e.g.
from package glmmTMB), this would return the expected response
mu*(1-p) on the population-level. See 'Details'.
"zi_random" (or "zero_inflated_random")
This only applies to mixed models. Predicted values are conditioned on
the fixed effects and the zero-inflation component. Use this for
unit-level predictions, i.e. predicted values for each level of the
random effects groups. Add the name of the related random effect term to
the terms-argument (for more details, see this vignette).
"zi_prob"
Returns the predicted zero-inflation probability, i.e. probability of a structural or "true" zero (see this vignette for a short introduction into zero-inflation models).
"simulate"
Predicted values and confidence resp. prediction intervals are based on
simulations, i.e. calls to simulate(). This type of prediction takes
all model uncertainty into account. Currently supported models are
objects of class lm, glm, glmmTMB, wbm, MixMod and merMod.
Use nsim to set the number of simulated draws (see ... for details).
"survival", "cumulative_hazard" and "quantile"
"survival" and "cumulative_hazard" apply only to coxph-objects from
the survial-package. These options calculate the survival probability
or the cumulative hazard of an event. type = "quantile" only applies to
survreg-objects from package survival, which returns the predicted
quantiles. For this option, the p argument is passed to predict(),
so that quantiles for different probabilities can be calculated, e.g.
predict_response(..., type = "quantile", p = c(0.2, 0.5, 0.8)).
When margin = "empirical" (or when calling ggaverage()), the type
argument accepts all values from the type-argument of the model's respective
predict()-method.
Character vector, naming the function to be applied to the
covariates (non-focal terms) over which the effect is "averaged". The
default is "mean". Can be "mean", "weighted.mean", "median", "mode"
or "zero", which call the corresponding R functions (except "mode",
which calls an internal function to compute the most common value); "zero"
simply returns 0. By default, if the covariate is a factor, only "mode" is
applicable; for all other values (including the default, "mean") the
reference level is returned. For character vectors, only the mode is returned.
You can use a named vector to apply different functions to integer, numeric and
categorical covariates, e.g. typical = c(numeric = "median", factor = "mode").
If typical is "weighted.mean", weights from the model are used. If no
weights are available, the function falls back to "mean". Note that this
argument is ignored for predict_response(), because the margin argument
takes care of this.
Named character vector, which indicates covariates that
should be held constant at specific values. Unlike typical, which
applies a function to the covariates to determine the value that is used
to hold these covariates constant, condition can be used to define
exact values, for instance condition = c(covariate1 = 20, covariate2 = 5).
See 'Examples'.
Logical, if TRUE (the default), predicted values for
log-, log-log, exp, sqrt and similar transformed responses will be
back-transformed to original response-scale. See
insight::find_transformation() for more details.
Variance-covariance matrix used to compute uncertainty estimates (e.g., for confidence intervals based on robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix.
A covariance matrix
A function which returns a covariance matrix (e.g., stats::vcov())
A string which indicates the kind of uncertainty estimates to return.
Heteroskedasticity-consistent: "HC", "HC0", "HC1", "HC2",
"HC3", "HC4", "HC4m", "HC5". See ?sandwich::vcovHC
Cluster-robust: "vcovCR", "CR0", "CR1", "CR1p", "CR1S",
"CR2", "CR3". See ?clubSandwich::vcovCR.
Bootstrap: "BS", "xy", "fractional", "jackknife", "residual",
"wild", "mammen", "norm", "webb". See ?sandwich::vcovBS
Other sandwich package functions: "HAC", "PC", "CL", or "PL".
If NULL, standard errors (and confidence intervals) for predictions are
based on the standard errors as returned by the predict()-function.
Note that probably not all model objects that work with
predict_response() are also supported by the sandwich or
clubSandwich packages.
See details in this vignette.
List of arguments to be passed to the function identified by
the vcov argument. This function is typically supplied by the
sandwich or clubSandwich packages. Please refer to their
documentation (e.g., ?sandwich::vcovHAC) to see the list of available
arguments. If no estimation type (argument type) is given, the default
type for "HC" equals the default from the sandwich package; for type
"CR" the default is set to "CR3". For other defaults, refer to the
documentation in the sandwich or clubSandwich package.
This argument is used in two different ways, depending on the
margin argument.
When margin = "empirical" (or when calling ggaverag()), weights can
either be a character vector, naming the weigthing variable in the data, or
a vector of weights (of same length as the number of observations in the
data). This variable will be used to weight adjusted predictions.
When margin = "marginalmeans" (or when calling ggemmeans()), weights
must be a character vector and is passed to emmeans::emmeans(),
specifying weights to use in averaging non-focal categorical predictors.
Options are "equal", "proportional", "outer", "cells", "flat",
and "show.levels". See ?emmeans::emmeans for details.
Toggle messages or warnings.
Logical, if TRUE, adjusts for bias-correction when
back-transforming the predicted values (to the response scale) for
non-Gaussian mixed models. Back-transforming the the population-level
predictions ignores the effect of the variation around the population mean,
so the result on the original data scale is biased due to Jensen's
inequality. That means, when type = "fixed" (the default) and population
level predictions are returned, it is recommended to set bias_correction = TRUE.
To apply bias-correction, a valid value of sigma is required, which is
extracted by default using insight::get_variance_residual(). Optionally,
to provide own estimates of uncertainty, use the sigma argument. Note that
bias_correction currently only applies to mixed models, where there are
additive random components involved and where that bias-adjustment can be
appropriate. If ggemmeans() is called, bias-correction can also be applied
to GEE-models.
Type of interval calculation, can either be "confidence"
(default) or "prediction". May be abbreviated. Unlike confidence
intervals, prediction intervals include the residual variance (sigma^2) to
account for the uncertainty of predicted values. Note that prediction
intervals are not available for all models, but only for models that work
with insight::get_sigma(). For Bayesian models, when interval = "confidence", predictions are based on posterior draws of the linear
predictor rstantools::posterior_epred(). If interval = "prediction",
rstantools::posterior_predict() is called.
Please see ?predict_response for details and examples.