ggpredict: Marginal effects, adjusted predictions and estimated marginal...
In ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
as.data.frame.ggeffects R Documentation
Marginal effects, adjusted predictions and estimated marginal means from regression models
Description
The ggeffects package computes estimated marginal means (predicted values)
for the response, at the margin of specific values or levels from certain
model terms, i.e. it generates predictions by a model by holding the
non-focal variables constant and varying the focal variable(s).
ggpredict() uses predict() for generating predictions,
while ggeffect() computes marginal effects by internally calling
effects::Effect() and ggemmeans() uses emmeans::emmeans().
The result is returned as consistent data frame.
Usage
## S3 method for class 'ggeffects'
as.data.frame(
x,
row.names = NULL,
optional = FALSE,
...,
stringsAsFactors = FALSE,
terms_to_colnames = FALSE
)
ggeffect(model, terms, ci_level = 0.95, verbose = TRUE, ci.lvl = ci_level, ...)
ggemmeans(
model,
terms,
ci_level = 0.95,
type = "fixed",
typical = "mean",
condition = NULL,
back_transform = TRUE,
interval = "confidence",
verbose = TRUE,
ci.lvl = ci_level,
back.transform = back_transform,
...
)
ggpredict(
model,
terms,
ci_level = 0.95,
type = "fixed",
typical = "mean",
condition = NULL,
back_transform = TRUE,
ppd = FALSE,
vcov_fun = NULL,
vcov_type = NULL,
vcov_args = NULL,
interval,
verbose = TRUE,
ci.lvl = ci_level,
back.transform = back_transform,
vcov.fun = vcov_fun,
vcov.type = vcov_type,
vcov.args = vcov_args,
...
)
Arguments
x
An object of class ggeffects, as returned by ggpredict(),
ggeffect() or ggemmeans().
row.names
NULL or a character vector giving the row
names for the data frame. Missing values are not allowed.
optional
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.
...
For ggpredict(), further arguments passed down to
predict(); for ggeffect(), further arguments passed
down to effects::Effect(); and for ggemmeans(),
further arguments passed down to emmeans::emmeans().
If type = "sim", ... may also be used to set the number of
simulation, e.g. nsim = 500.
stringsAsFactors
logical: should the character vector be converted
to a factor?
terms_to_colnames
Logical, if TRUE, standardized column names (like
"x", "group" or "facet") are replaced by the variable names of the focal
predictors specified in terms.
model
A fitted model object, or a list of model objects. Any model
that supports common methods like predict(), family()
or model.frame() should work. For ggeffect(), any model
that is supported by effects should work, and for
ggemmeans(), all models supported by emmeans should work.
terms
Character vector, (or a named list or a formula) with the names
of those terms from model, for which predictions should be displayed (so
calles focal terms). At least one term is required to calculate effects
for certain terms, maximum length is four terms, where the second to fourth
term indicate the groups, i.e. predictions of first term are grouped at
meaningful values or levels of the remaining terms (see values_at()).
If terms is missing or NULL, adjusted predictions for each model term
are calculated (i.e. each model term is used as single focal term). It is
also possible to define specific values for focal terms, at which adjusted
predictions should be calculated (see 'Details'). All remaining covariates
that are not specified in terms are held constant (see 'Details'). See
also arguments condition and typical.
ci_level
Numeric, the level of the confidence intervals. For ggpredict(),
use ci_level = NA, if confidence intervals should not be calculated
(for instance, due to computation time). Typically, confidence intervals
based on the standard errors as returned by the predict() function
are returned, assuming normal distribution (i.e. +/- 1.96 * SE).
See introduction of this vignette
for more details.
verbose
Toggle messages or warnings.
ci.lvl, vcov.fun, vcov.type, vcov.args, back.transform
Deprecated arguments.
Please use ci_level, vcov_fun, vcov_type, vcov_args and back_transform
instead.
type
Character, only applies for survival models, mixed effects models
and/or models with zero-inflation. Note: For brmsfit-models
with zero-inflation component, there is no type = "zero_inflated" nor
type = "zi_random"; predicted values for MixMod-models from
GLMMadaptive with zero-inflation component always condition on
the zero-inflation part of the model (see 'Details').
-
"fixed" (or "fe" or "count")
Predicted values are conditioned on the fixed effects or conditional
model only (for mixed models: predicted values are on the population-level
and confidence intervals are returned, i.e. re.form = NA when calling
predict()). For instance, for models fitted with zeroinfl from pscl,
this would return the predicted mean from the count component (without
zero-inflation). For models with zero-inflation component, this type calls
predict(..., type = "link") (however, predicted values are
back-transformed to the response scale).
-
"random" (or "re")
This only applies to mixed models, and type = "random" does not condition
on the zero-inflation component of the model. type = "random" still
returns population-level predictions, however, conditioned on random effects
and considering individual level predictions, i.e. re.form = NULL when
calling predict(). This may affect the returned predicted values, depending
on whether REML = TRUE or REML = FALSE was used for model fitting.
Furthermore, unlike type = "fixed", intervals also consider the uncertainty
in the variance parameters (the mean random effect variance, see Johnson
et al. 2014 for details) and hence can be considered as prediction intervals.
For models with zero-inflation component, this type calls
predict(..., type = "link") (however, predicted values are back-transformed
to the response scale).
To get 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 "fe.zi" or "zi")
Predicted values are conditioned on the fixed effects and the zero-inflation
component. For instance, for models fitted with zeroinfl
from pscl, this would return the predicted response (mu*(1-p))
and for glmmTMB, this would return the expected value mu*(1-p)
without conditioning on random effects (i.e. random effect variances
are not taken into account for the confidence intervals). For models with
zero-inflation component, this type calls predict(..., type = "response").
See 'Details'.
-
"zi_random" (or "re.zi" or "zero_inflated_random")
Predicted values are conditioned on the zero-inflation component and
take the random effects uncertainty into account. For models fitted with
glmmTMB(), hurdle() or zeroinfl(), this would return the
expected value mu*(1-p). For glmmTMB, prediction intervals
also consider the uncertainty in the random effects variances. This
type calls predict(..., type = "response"). See 'Details'.
-
"zi_prob" (or "zi.prob")
Predicted zero-inflation probability. For glmmTMB models with
zero-inflation component, this type calls predict(..., type = "zlink");
models from pscl call predict(..., type = "zero") and for
GLMMadaptive, predict(..., type = "zero_part") is called.
-
"simulate" (or "sim")
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, including
random effects variances. Currently supported models are objects of
class lm, glm, glmmTMB, wbm, MixMod
and merMod. See ... for details on number of simulations.
-
"survival" and "cumulative_hazard" (or "surv" and "cumhaz")
Applies only to coxph-objects from the survial-package and
calculates the survival probability or the cumulative hazard of an event.
typical
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".
condition
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'.
back_transform
Logical, if TRUE (the default), predicted values
for log- or log-log transformed responses will be back-transformed to
original response-scale.
interval
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. For mixed models, interval = "prediction"
is the default for type = "random". When type = "fixed", the default is
interval = "confidence". Note that prediction intervals are not available
for all models, but only for models that work with insight::get_sigma().
ppd
Logical, if TRUE, predictions for Stan-models are
based on the posterior predictive distribution
rstantools::posterior_predict(). If FALSE (the
default), predictions are based on posterior draws of the linear
predictor rstantools::posterior_linpred().
vcov_fun
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 (variance-covariance) matrix
A function which returns a covariance matrix (e.g., stats::vcov())
A string which indicates the name of the vcov*()-function from the
sandwich or clubSandwich packages, e.g. vcov_fun = "vcovCL",
which is used to compute (cluster) robust standard errors for predictions.
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 ggpredict()
are also supported by the sandwich or clubSandwich packages.
See details in this vignette.
vcov_type
Character vector, specifying the estimation type for the
robust covariance matrix estimation (see ?sandwich::vcovHC
or ?clubSandwich::vcovCR for details). Only used when vcov_fun is a
character string indicating on of the function from those packages.
vcov_args
List of named vectors, used as additional arguments that
are passed down to vcov_fun.
Details
Supported Models
A list of supported models can be found at the package website.
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.
Difference between ggpredict() and ggeffect() or ggemmeans()
ggpredict() calls predict(), while ggeffect() calls effects::Effect()
and ggemmeans() calls emmeans::emmeans() to compute predicted values.
Thus, effects returned by ggpredict() can be described as conditional effects
(i.e. these are conditioned on certain (reference) levels of factors), while
ggemmeans() and ggeffect() return marginal means, since
the effects are "marginalized" (or "averaged") over the levels of factors
(or values of character vectors). Therefore, ggpredict() and ggeffect()
resp. ggemmeans() differ in how factors and character vectors are held
constant: ggpredict() uses the reference level (or "lowest" value in case
of character vectors), while ggeffect() and ggemmeans() compute a
kind of "average" value, which represents the proportions of each factor's
category. Use condition to set a specific level for factors in
ggemmeans(), so factors are not averaged over their categories,
but held constant at a given level.
Marginal Effects and Adjusted Predictions at Specific Values
Meaningful values of focal terms can be specified via the terms argument.
Specifying meaningful or representative values as string pattern is the
preferred way in the ggeffects package. However, it is also possible to
use a list() for the focal terms if prefer the "classical" R way, which is
described in this vignette.
Indicating levels in square brackets allows for selecting only certain
groups or values resp. value ranges. The term name and the start of the
levels in brackets must be separated by a whitespace character, e.g.
terms = c("age", "education [1,3]"). Numeric ranges, separated with colon,
are also allowed: terms = c("education", "age [30:60]"). The stepsize for
ranges can be adjusted using by, e.g. terms = "age [30:60 by=5]".
The terms argument also supports the same shortcuts as the values argument
in values_at(). So terms = "age [meansd]" would return predictions for
the values one standard deviation below the mean age, the mean age and one SD
above the mean age. terms = "age [quart2]" would calculate predictions at
the value of the lower, median and upper quartile of age.
Furthermore, it is possible to specify a function name. Values for predictions
will then be transformed, e.g. terms = "income [exp]". This is useful when
model predictors were transformed for fitting the model and should be
back-transformed to the original scale for predictions. It is also possible
to define own functions (see
this vignette).
Instead of a function, it is also possible to define the name of a variable
with specific values, e.g. to define a vector v = c(1000, 2000, 3000) and
then use terms = "income [v]".
You can take a random sample of any size with sample=n, e.g
terms = "income [sample=8]", which will sample eight values from
all possible values of the variable income. This option is especially
useful for plotting predictions at certain levels of random effects
group levels, where the group factor has many levels that can be completely
plotted. For more details, see
this vignette.
Finally, numeric vectors for which no specific values are given, a "pretty range"
is calculated (see pretty_range()), to avoid memory allocation problems
for vectors with many unique values. If a numeric vector is specified as
second or third term (i.e. if this vector represents a grouping structure),
representative values (see values_at()) are chosen (unless other values
are specified). If all values for a numeric vector should be used to compute
predictions, you may use e.g. terms = "age [all]". See also package vignettes.
To create a pretty range that should be smaller or larger than the default
range (i.e. if no specific values would be given), use the n tag, e.g.
terms="age [n=5]" or terms="age [n=12]". Larger values for n return a
larger range of predicted values.
Holding covariates at constant values
For ggpredict(), a data grid is constructed, roughly comparable to
expand.grid() on all unique combinations of model.frame(model)[, terms].
This data grid (see data_grid()) as newdata argument for predict().
In this case, all remaining covariates that are not specified in terms are
held constant: Numeric values are set to the mean (unless changed with
the condition or typical argument), integer values are set to their
median, factors are set to their reference level (may also be changed with
condition) and character vectors to their mode (most common element).
ggeffect() and ggemmeans(), by default, set remaining numeric covariates
to their mean value, while for factors, a kind of "average" value, which
represents the proportions of each factor's category, is used. The same
applies to character vectors: ggemmeans() averages over the distribution
of unique values in a character vector, similar to how factors are treated.
For ggemmeans(), use condition to set a specific level for
factors so that these are not averaged over their categories, but held
constant at the given level.
Bayesian Regression Models
ggpredict() also works with Stan-models from the rstanarm or
brms-packages. The predicted values are the median value of all drawn
posterior samples. The confidence intervals for Stan-models are Bayesian
predictive intervals. By default (i.e. ppd = FALSE), the predictions are
based on rstantools::posterior_linpred() and hence have some limitations:
the uncertainty of the error term is not taken into account. The recommendation
is to use the posterior predictive distribution (rstantools::posterior_predict()).
Zero-Inflated and Zero-Inflated Mixed Models with brms
Models of class brmsfit always condition on the zero-inflation component,
if the model has such a component. Hence, there is no type = "zero_inflated"
nor type = "zi_random" for brmsfit-models, because predictions are based
on draws of the posterior distribution, which already account for the
zero-inflation part of the model.
Zero-Inflated and Zero-Inflated Mixed Models with glmmTMB
If model is of class glmmTMB, hurdle, zeroinfl or zerotrunc,
simulations from a multivariate normal distribution (see ?MASS::mvrnorm)
are drawn to calculate mu*(1-p). Confidence intervals are then based on
quantiles of these results. For type = "zi_random", prediction intervals
also take the uncertainty in the random-effect paramters into account (see
also Brooks et al. 2017, pp.391-392 for details).
An alternative for models fitted with glmmTMB that take all model
uncertainties into account are simulations based on simulate(), which
is used when type = "sim" (see Brooks et al. 2017, pp.392-393 for
details).
MixMod-models from GLMMadaptive
Predicted values for the fixed effects component (type = "fixed" or
type = "zero_inflated") are based on predict(..., type = "mean_subject"),
while predicted values for random effects components (type = "random" or
type = "zi_random") are calculated with predict(..., type = "subject_specific")
(see ?GLMMadaptive::predict.MixMod for details). The latter option
requires the response variable to be defined in the newdata-argument
of predict(), which will be set to its typical value (see
values_at()).
Value
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.
Note
Multinomial Models
polr, clm models, or more generally speaking, models with ordinal or
multinominal outcomes, have an additional column response.level, which
indicates with which level of the response variable the predicted values are
associated.
Printing Results
The print() method gives a clean output (especially for predictions by
groups), and indicates at which values covariates were held constant.
Furthermore, the print() method has the arguments digits and n to
control number of decimals and lines to be printed.
Limitations
The support for some models, for example from package MCMCglmm, is
rather experimental and may fail for certain models. If you encounter
any errors, please file an issue at Github.
References
Brooks ME, Kristensen K, Benthem KJ van, Magnusson A, Berg CW, Nielsen A,
et al. glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated
Generalized Linear Mixed Modeling. The R Journal. 2017;9: 378-400.
Johnson PC, O'Hara RB. 2014. Extension of Nakagawa & Schielzeth's R2GLMM
to random slopes models. Methods Ecol Evol, 5: 944-946.
Examples
library(sjlabelled)
data(efc)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
ggpredict(fit, terms = "c12hour")
ggpredict(fit, terms = c("c12hour", "c172code"))
ggpredict(fit, terms = c("c12hour", "c172code", "c161sex"))
# specified as formula
ggpredict(fit, terms = ~ c12hour + c172code + c161sex)
# only range of 40 to 60 for variable 'c12hour'
ggpredict(fit, terms = "c12hour [40:60]")
# terms as named list
ggpredict(fit, terms = list(c12hour = 40:60))
# covariate "neg_c_7" is held constant at a value of 11.84 (its mean value).
# To use a different value, use "condition"
ggpredict(fit, terms = "c12hour [40:60]", condition = c(neg_c_7 = 20))
# to plot ggeffects-objects, you can use the 'plot()'-function.
# the following examples show how to build your ggplot by hand.
# plot predicted values, remaining covariates held constant
library(ggplot2)
mydf <- ggpredict(fit, terms = "c12hour")
ggplot(mydf, aes(x, predicted)) +
geom_line() +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.1)
# three variables, so we can use facets and groups
mydf <- ggpredict(fit, terms = c("c12hour", "c161sex", "c172code"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE) +
facet_wrap(~facet, ncol = 2)
# select specific levels for grouping terms
mydf <- ggpredict(fit, terms = c("c12hour", "c172code [1,3]", "c161sex"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE) +
facet_wrap(~facet) +
labs(
y = get_y_title(mydf),
x = get_x_title(mydf),
colour = get_legend_title(mydf)
)
# level indication also works for factors with non-numeric levels
# and in combination with numeric levels for other variables
data(efc)
efc$c172code <- sjlabelled::as_label(efc$c172code)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
ggpredict(fit, terms = c("c12hour",
"c172code [low level of education, high level of education]",
"c161sex [1]"))
# when "terms" is a named list
ggpredict(fit, terms = list(
c12hour = seq(0, 170, 30),
c172code = c("low level of education", "high level of education"),
c161sex = 1)
)
# use categorical value on x-axis, use axis-labels, add error bars
dat <- ggpredict(fit, terms = c("c172code", "c161sex"))
ggplot(dat, aes(x, predicted, colour = group)) +
geom_point(position = position_dodge(0.1)) +
geom_errorbar(
aes(ymin = conf.low, ymax = conf.high),
position = position_dodge(0.1)
) +
scale_x_discrete(breaks = 1:3, labels = get_x_labels(dat))
# 3-way-interaction with 2 continuous variables
data(efc)
# make categorical
efc$c161sex <- as_factor(efc$c161sex)
fit <- lm(neg_c_7 ~ c12hour * barthtot * c161sex, data = efc)
# select only levels 30, 50 and 70 from continuous variable Barthel-Index
dat <- ggpredict(fit, terms = c("c12hour", "barthtot [30,50,70]", "c161sex"))
ggplot(dat, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE, fullrange = TRUE) +
facet_wrap(~facet) +
labs(
colour = get_legend_title(dat),
x = get_x_title(dat),
y = get_y_title(dat),
title = get_title(dat)
)
# or with ggeffects' plot-method
plot(dat, ci = FALSE)
# predictions for polynomial terms
data(efc)
fit <- glm(
tot_sc_e ~ c12hour + e42dep + e17age + I(e17age^2) + I(e17age^3),
data = efc,
family = poisson()
)
ggeffect(fit, terms = "e17age")
ggeffects documentation built on Oct. 17, 2023, 5:07 p.m.
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| as.data.frame.ggeffects | R Documentation |
Marginal effects, adjusted predictions and estimated marginal means from regression models
Description
The ggeffects package computes estimated marginal means (predicted values) for the response, at the margin of specific values or levels from certain model terms, i.e. it generates predictions by a model by holding the non-focal variables constant and varying the focal variable(s).
ggpredict() uses predict() for generating predictions,
while ggeffect() computes marginal effects by internally calling
effects::Effect() and ggemmeans() uses emmeans::emmeans().
The result is returned as consistent data frame.
Usage
## S3 method for class 'ggeffects'
as.data.frame(
x,
row.names = NULL,
optional = FALSE,
...,
stringsAsFactors = FALSE,
terms_to_colnames = FALSE
)
ggeffect(model, terms, ci_level = 0.95, verbose = TRUE, ci.lvl = ci_level, ...)
ggemmeans(
model,
terms,
ci_level = 0.95,
type = "fixed",
typical = "mean",
condition = NULL,
back_transform = TRUE,
interval = "confidence",
verbose = TRUE,
ci.lvl = ci_level,
back.transform = back_transform,
...
)
ggpredict(
model,
terms,
ci_level = 0.95,
type = "fixed",
typical = "mean",
condition = NULL,
back_transform = TRUE,
ppd = FALSE,
vcov_fun = NULL,
vcov_type = NULL,
vcov_args = NULL,
interval,
verbose = TRUE,
ci.lvl = ci_level,
back.transform = back_transform,
vcov.fun = vcov_fun,
vcov.type = vcov_type,
vcov.args = vcov_args,
...
)
Arguments
x |
An object of class |
row.names |
|
optional |
logical. If |
... |
For |
stringsAsFactors |
logical: should the character vector be converted to a factor? |
terms_to_colnames |
Logical, if |
model |
A fitted model object, or a list of model objects. Any model
that supports common methods like |
terms |
Character vector, (or a named list or a formula) with the names
of those terms from |
ci_level |
Numeric, the level of the confidence intervals. For |
verbose |
Toggle messages or warnings. |
ci.lvl, vcov.fun, vcov.type, vcov.args, back.transform |
Deprecated arguments.
Please use |
type |
Character, only applies for survival models, mixed effects models
and/or models with zero-inflation. Note: For
|
typical |
Character vector, naming the function to be applied to the
covariates (non-focal terms) over which the effect is "averaged". The
default is |
condition |
Named character vector, which indicates covariates that
should be held constant at specific values. Unlike |
back_transform |
Logical, if |
interval |
Type of interval calculation, can either be |
ppd |
Logical, if |
vcov_fun |
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.
See details in this vignette. |
vcov_type |
Character vector, specifying the estimation type for the
robust covariance matrix estimation (see |
vcov_args |
List of named vectors, used as additional arguments that
are passed down to |
Details
Supported Models
A list of supported models can be found at the package website.
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.
Difference between ggpredict() and ggeffect() or ggemmeans()
ggpredict() calls predict(), while ggeffect() calls effects::Effect()
and ggemmeans() calls emmeans::emmeans() to compute predicted values.
Thus, effects returned by ggpredict() can be described as conditional effects
(i.e. these are conditioned on certain (reference) levels of factors), while
ggemmeans() and ggeffect() return marginal means, since
the effects are "marginalized" (or "averaged") over the levels of factors
(or values of character vectors). Therefore, ggpredict() and ggeffect()
resp. ggemmeans() differ in how factors and character vectors are held
constant: ggpredict() uses the reference level (or "lowest" value in case
of character vectors), while ggeffect() and ggemmeans() compute a
kind of "average" value, which represents the proportions of each factor's
category. Use condition to set a specific level for factors in
ggemmeans(), so factors are not averaged over their categories,
but held constant at a given level.
Marginal Effects and Adjusted Predictions at Specific Values
Meaningful values of focal terms can be specified via the terms argument.
Specifying meaningful or representative values as string pattern is the
preferred way in the ggeffects package. However, it is also possible to
use a list() for the focal terms if prefer the "classical" R way, which is
described in this vignette.
Indicating levels in square brackets allows for selecting only certain
groups or values resp. value ranges. The term name and the start of the
levels in brackets must be separated by a whitespace character, e.g.
terms = c("age", "education [1,3]"). Numeric ranges, separated with colon,
are also allowed: terms = c("education", "age [30:60]"). The stepsize for
ranges can be adjusted using by, e.g. terms = "age [30:60 by=5]".
The terms argument also supports the same shortcuts as the values argument
in values_at(). So terms = "age [meansd]" would return predictions for
the values one standard deviation below the mean age, the mean age and one SD
above the mean age. terms = "age [quart2]" would calculate predictions at
the value of the lower, median and upper quartile of age.
Furthermore, it is possible to specify a function name. Values for predictions
will then be transformed, e.g. terms = "income [exp]". This is useful when
model predictors were transformed for fitting the model and should be
back-transformed to the original scale for predictions. It is also possible
to define own functions (see
this vignette).
Instead of a function, it is also possible to define the name of a variable
with specific values, e.g. to define a vector v = c(1000, 2000, 3000) and
then use terms = "income [v]".
You can take a random sample of any size with sample=n, e.g
terms = "income [sample=8]", which will sample eight values from
all possible values of the variable income. This option is especially
useful for plotting predictions at certain levels of random effects
group levels, where the group factor has many levels that can be completely
plotted. For more details, see
this vignette.
Finally, numeric vectors for which no specific values are given, a "pretty range"
is calculated (see pretty_range()), to avoid memory allocation problems
for vectors with many unique values. If a numeric vector is specified as
second or third term (i.e. if this vector represents a grouping structure),
representative values (see values_at()) are chosen (unless other values
are specified). If all values for a numeric vector should be used to compute
predictions, you may use e.g. terms = "age [all]". See also package vignettes.
To create a pretty range that should be smaller or larger than the default
range (i.e. if no specific values would be given), use the n tag, e.g.
terms="age [n=5]" or terms="age [n=12]". Larger values for n return a
larger range of predicted values.
Holding covariates at constant values
For ggpredict(), a data grid is constructed, roughly comparable to
expand.grid() on all unique combinations of model.frame(model)[, terms].
This data grid (see data_grid()) as newdata argument for predict().
In this case, all remaining covariates that are not specified in terms are
held constant: Numeric values are set to the mean (unless changed with
the condition or typical argument), integer values are set to their
median, factors are set to their reference level (may also be changed with
condition) and character vectors to their mode (most common element).
ggeffect() and ggemmeans(), by default, set remaining numeric covariates
to their mean value, while for factors, a kind of "average" value, which
represents the proportions of each factor's category, is used. The same
applies to character vectors: ggemmeans() averages over the distribution
of unique values in a character vector, similar to how factors are treated.
For ggemmeans(), use condition to set a specific level for
factors so that these are not averaged over their categories, but held
constant at the given level.
Bayesian Regression Models
ggpredict() also works with Stan-models from the rstanarm or
brms-packages. The predicted values are the median value of all drawn
posterior samples. The confidence intervals for Stan-models are Bayesian
predictive intervals. By default (i.e. ppd = FALSE), the predictions are
based on rstantools::posterior_linpred() and hence have some limitations:
the uncertainty of the error term is not taken into account. The recommendation
is to use the posterior predictive distribution (rstantools::posterior_predict()).
Zero-Inflated and Zero-Inflated Mixed Models with brms
Models of class brmsfit always condition on the zero-inflation component,
if the model has such a component. Hence, there is no type = "zero_inflated"
nor type = "zi_random" for brmsfit-models, because predictions are based
on draws of the posterior distribution, which already account for the
zero-inflation part of the model.
Zero-Inflated and Zero-Inflated Mixed Models with glmmTMB
If model is of class glmmTMB, hurdle, zeroinfl or zerotrunc,
simulations from a multivariate normal distribution (see ?MASS::mvrnorm)
are drawn to calculate mu*(1-p). Confidence intervals are then based on
quantiles of these results. For type = "zi_random", prediction intervals
also take the uncertainty in the random-effect paramters into account (see
also Brooks et al. 2017, pp.391-392 for details).
An alternative for models fitted with glmmTMB that take all model
uncertainties into account are simulations based on simulate(), which
is used when type = "sim" (see Brooks et al. 2017, pp.392-393 for
details).
MixMod-models from GLMMadaptive
Predicted values for the fixed effects component (type = "fixed" or
type = "zero_inflated") are based on predict(..., type = "mean_subject"),
while predicted values for random effects components (type = "random" or
type = "zi_random") are calculated with predict(..., type = "subject_specific")
(see ?GLMMadaptive::predict.MixMod for details). The latter option
requires the response variable to be defined in the newdata-argument
of predict(), which will be set to its typical value (see
values_at()).
Value
A data frame (with ggeffects class attribute) with consistent data columns:
-
"x": the values of the first term interms, 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 interms, used as grouping-aesthetics in plots. -
"facet": the grouping level from the third term interms, 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 classggeffects, which has anterms_to_colnamesargument, to use the term names as column names instead of the standardized names"x"etc.
Note
Multinomial Models
polr, clm models, or more generally speaking, models with ordinal or
multinominal outcomes, have an additional column response.level, which
indicates with which level of the response variable the predicted values are
associated.
Printing Results
The print() method gives a clean output (especially for predictions by
groups), and indicates at which values covariates were held constant.
Furthermore, the print() method has the arguments digits and n to
control number of decimals and lines to be printed.
Limitations
The support for some models, for example from package MCMCglmm, is rather experimental and may fail for certain models. If you encounter any errors, please file an issue at Github.
References
Brooks ME, Kristensen K, Benthem KJ van, Magnusson A, Berg CW, Nielsen A, et al. glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal. 2017;9: 378-400.
Johnson PC, O'Hara RB. 2014. Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models. Methods Ecol Evol, 5: 944-946.
Examples
library(sjlabelled)
data(efc)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
ggpredict(fit, terms = "c12hour")
ggpredict(fit, terms = c("c12hour", "c172code"))
ggpredict(fit, terms = c("c12hour", "c172code", "c161sex"))
# specified as formula
ggpredict(fit, terms = ~ c12hour + c172code + c161sex)
# only range of 40 to 60 for variable 'c12hour'
ggpredict(fit, terms = "c12hour [40:60]")
# terms as named list
ggpredict(fit, terms = list(c12hour = 40:60))
# covariate "neg_c_7" is held constant at a value of 11.84 (its mean value).
# To use a different value, use "condition"
ggpredict(fit, terms = "c12hour [40:60]", condition = c(neg_c_7 = 20))
# to plot ggeffects-objects, you can use the 'plot()'-function.
# the following examples show how to build your ggplot by hand.
# plot predicted values, remaining covariates held constant
library(ggplot2)
mydf <- ggpredict(fit, terms = "c12hour")
ggplot(mydf, aes(x, predicted)) +
geom_line() +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.1)
# three variables, so we can use facets and groups
mydf <- ggpredict(fit, terms = c("c12hour", "c161sex", "c172code"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE) +
facet_wrap(~facet, ncol = 2)
# select specific levels for grouping terms
mydf <- ggpredict(fit, terms = c("c12hour", "c172code [1,3]", "c161sex"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE) +
facet_wrap(~facet) +
labs(
y = get_y_title(mydf),
x = get_x_title(mydf),
colour = get_legend_title(mydf)
)
# level indication also works for factors with non-numeric levels
# and in combination with numeric levels for other variables
data(efc)
efc$c172code <- sjlabelled::as_label(efc$c172code)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
ggpredict(fit, terms = c("c12hour",
"c172code [low level of education, high level of education]",
"c161sex [1]"))
# when "terms" is a named list
ggpredict(fit, terms = list(
c12hour = seq(0, 170, 30),
c172code = c("low level of education", "high level of education"),
c161sex = 1)
)
# use categorical value on x-axis, use axis-labels, add error bars
dat <- ggpredict(fit, terms = c("c172code", "c161sex"))
ggplot(dat, aes(x, predicted, colour = group)) +
geom_point(position = position_dodge(0.1)) +
geom_errorbar(
aes(ymin = conf.low, ymax = conf.high),
position = position_dodge(0.1)
) +
scale_x_discrete(breaks = 1:3, labels = get_x_labels(dat))
# 3-way-interaction with 2 continuous variables
data(efc)
# make categorical
efc$c161sex <- as_factor(efc$c161sex)
fit <- lm(neg_c_7 ~ c12hour * barthtot * c161sex, data = efc)
# select only levels 30, 50 and 70 from continuous variable Barthel-Index
dat <- ggpredict(fit, terms = c("c12hour", "barthtot [30,50,70]", "c161sex"))
ggplot(dat, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE, fullrange = TRUE) +
facet_wrap(~facet) +
labs(
colour = get_legend_title(dat),
x = get_x_title(dat),
y = get_y_title(dat),
title = get_title(dat)
)
# or with ggeffects' plot-method
plot(dat, ci = FALSE)
# predictions for polynomial terms
data(efc)
fit <- glm(
tot_sc_e ~ c12hour + e42dep + e17age + I(e17age^2) + I(e17age^3),
data = efc,
family = poisson()
)
ggeffect(fit, terms = "e17age")
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