as.data.frame.ggeffects  R Documentation 
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
zeroinflation 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 class '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_fun = NULL,
vcov_type = NULL,
vcov_args = NULL,
weights = NULL,
verbose = TRUE,
...
)
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,
...
)
x 
An object of class 
row.names 

optional 
logical. If 
... 
Arguments are passed down to 
stringsAsFactors 
logical: should the character vector be converted to a factor? 
terms_to_colnames 
Logical, if 
model 
A model object, or a list of model objects. 
terms 
Names of those terms from
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 
ci_level 
Numeric, the level of the confidence intervals. Use

type 
Character, indicating whether predictions should be conditioned
on specific model components or not. Consequently, most options only apply
for survival models, mixed effects models and/or models with zeroinflation
(and their Bayesian counterparts); only exeption is Note: For

typical 
Character vector, naming the function to be applied to the
covariates (nonfocal 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 
vcov_fun 
Variancecovariance 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.
If 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 
weights 
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). Only applies to 
verbose 
Toggle messages or warnings. 
ci.lvl , vcov.fun , vcov.type , vcov.args , back.transform 
Deprecated arguments.
Please use 
interval 
Type of interval calculation, can either be 
ppd 
Logical, if 
Please see ?predict_response
for details and examples.
A data frame (with ggeffects
class attribute) with consistent data columns:
"x"
: the values of the first term in terms
, used as xposition in plots.
"predicted"
: the predicted values of the response, used as yposition in plots.
"std.error"
: the standard error of the predictions. Note that the standard
errors are always on the linkscale, and not backtransformed for nonGaussian
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
groupingaesthetics 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.
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