plot_model() creates plots from regression models, either
estimates (as so-called forest or dot whisker plots) or marginal effects.
plot_model(model, type = c("est", "re", "eff", "pred", "int", "std",
"std2", "slope", "resid", "diag"), transform, terms = NULL,
sort.est = NULL, rm.terms = NULL, group.terms = NULL,
order.terms = NULL, pred.type = c("fe", "re"),
mdrt.values = c("minmax", "meansd", "zeromax", "quart", "all"),
ri.nr = NULL, title = NULL, axis.title = NULL,
axis.labels = NULL, legend.title = NULL, wrap.title = 50,
wrap.labels = 25, axis.lim = NULL, grid.breaks = NULL,
ci.lvl = NULL, se = NULL, colors = "Set1",
show.intercept = FALSE, show.values = FALSE, show.p = TRUE,
show.data = FALSE, show.legend = TRUE, show.zeroinf = TRUE,
value.offset = NULL, value.size, jitter = NULL, digits = 2,
dot.size = NULL, line.size = NULL, vline.color = NULL, grid, case,
auto.label = TRUE, prefix.labels = c("none", "varname", "label"),
bpe = "median", bpe.style = "line", bpe.color = "white", ...)get_model_data(model, type = c("est", "re", "eff", "pred", "int", "std",
"std2", "slope", "resid", "diag"), transform, terms = NULL,
sort.est = NULL, rm.terms = NULL, group.terms = NULL,
order.terms = NULL, pred.type = c("fe", "re"), ri.nr = NULL,
ci.lvl = NULL, colors = "Set1", grid, case = "parsed",
digits = 2, ...)
A regression model object. Depending on the type, many
kinds of models are supported, e.g. from packages like stats,
lme4, nlme, rstanarm, survey, glmmTMB,
MASS, brms etc.
Type of plot. There are three groups of plot-types: Coefficients (related vignette)
type = "est"Forest-plot of estimates. If the fitted model only contains one predictor, slope-line is plotted.
type = "re"For mixed effects models, plots the random effects.
type = "std"Forest-plot of standardized beta values.
type = "std2"Forest-plot of standardized beta values, however, standardization is done by dividing by two sd (see 'Details').
Marginal Effects (related vignette)
type = "pred"Predicted values (marginal effects) for
specific model terms. See ggpredict for details.
type = "eff"Similar to type = "pred", however,
discrete predictors are held constant at their proportions (not reference
level). See ggeffect for details.
type = "int"Marginal effects of interaction terms in
model.
Model diagnostics
type = "slope"Slope of coefficients for each single predictor, against the response (linear relationship between each model term and response).
type = "resid"Slope of coefficients for each single predictor, against the residuals (linear relationship between each model term and residuals).
type = "diag"Check model assumptions.
Note: For mixed models, the diagnostic plots like linear relationship or check for Homoscedasticity, do not take the uncertainty of random effects into account, but is only based on the fixed effects part of the model.
A character vector, naming a function that will be applied
on estimates and confidence intervals. By default, transform will
automatically use "exp" as transformation for applicable classes of
model (e.g. logistic or poisson regression). Estimates of linear
models remain untransformed. Use NULL if you want the raw,
non-transformed estimates.
Character vector with the names of those terms from model
that should be plotted. This argument depends on the plot-type:
Select terms that should be plotted. All other
term are removed from the output. Note that the term names must match
the names of the model's coefficients. For factors, this means that
the variable name is suffixed with the related factor level, and each
category counts as one term. E.g. rm.terms = "t_name [2,3]"
would remove the terms "t_name2" and "t_name3" (assuming
that the variable t_name is categorical and has at least
the factor levels 2 and 3). Another example for the
iris-dataset: terms = "Species" would not work, instead
you would write terms = "Species [versicolor,virginica]" to
remove these two levels, or terms = "Speciesversicolor" if you
just want to remove the level versicolor from the plot.
Here terms indicates for which
terms marginal effects should be displayed. At least one term is
required to calculate effects, maximum length is three terms, where
the second and third term indicate the groups, i.e. predictions of
first term are grouped by the levels of the second (and third) term.
terms may also indicate higher order terms (e.g. interaction
terms). Indicating levels in square brackets allows for selecting only
specific groups. Term name and levels in brackets must be separated by
a whitespace character, e.g. terms = c("age", "education [1,3]").
It is also possible to specify a range of numeric values for the
predictions with a colon, for instance terms = c("education [1,3]",
"age [30:50]"). 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. Finally, using pretty for numeric
variables (e.g. terms = "age [pretty]") calculates a pretty range
of values for the term, roughly of proportional length to the term's
value range. For more details, see ggpredict.
Determines in which way estimates are sorted in the plot:
If NULL (default), no sorting is done and estimates are sorted in the same order as they appear in the model formula.
If TRUE, estimates are sorted in descending order, with highest estimate at the top.
If sort.est = "sort.all", estimates are re-sorted for each coefficient (only applies if type = "re" and grid = FALSE), i.e. the estimates of the random effects for each predictor are sorted and plotted to an own plot.
If type = "re", specify a predictor's / coefficient's name to sort estimates according to this random effect.
Character vector with names that indicate which terms should
be removed from the plot. Counterpart to terms. rm.terms =
"t_name" would remove the term t_name. Default is NULL, i.e.
all terms are used. For factors, levels that should be removed from the plot
need to be explicitely indicated in square brackets, and match the model's
coefficient names, e.g. rm.terms = "t_name [2,3]" would remove the terms
"t_name2" and "t_name3" (assuming that the variable t_name
was categorical and has at least the factor levels 2 and 3).
Another example for the iris dataset would be
rm.terms = "Species [versicolor,virginica]". Note that the
rm.terms-argument does not apply to Marginal Effects plots.
Numeric vector with group indices, to group coefficients. Each group of coefficients gets its own color (see 'Examples').
Numeric vector, indicating in which order the coefficients should be plotted. See examples in this package-vignette.
Character, only applies for Marginal Effects plots
with mixed effects models. Indicates whether predicted values should be
conditioned on random effects (pred.type = "re") or fixed effects
only (pred.type = "fe", the default). For details, see documentation
of the type-argument in ggpredict.
Indicates which values of the moderator variable should be
used when plotting interaction terms (i.e. type = "int").
"minmax"(default) minimum and maximum values (lower and upper bounds) of the moderator are used to plot the interaction between independent variable and moderator(s).
"meansd"uses the mean value of the moderator as well as one standard deviation below and above mean value to plot the effect of the moderator on the independent variable (following the convention suggested by Cohen and Cohen and popularized by Aiken and West (1991), i.e. using the mean, the value one standard deviation above, and the value one standard deviation below the mean as values of the moderator, see Grace-Martin K: 3 Tips to Make Interpreting Moderation Effects Easier).
"zeromax"is similar to the "minmax" option, however,
0 is always used as minimum value for the moderator. This may be
useful for predictors that don't have an empirical zero-value, but absence
of moderation should be simulated by using 0 as minimum.
"quart"calculates and uses the quartiles (lower, median and upper) of the moderator value.
"all"uses all values of the moderator variable.
Numeric vector. If type = "re" and fitted model has more
than one random intercept, ri.nr indicates which random effects of
which random intercept (or: which list elements of
ranef) will be plotted. Default is NULL, so all
random effects will be plotted.
Character vector, used as plot title. By default,
get_dv_labels is called to retrieve the label of
the dependent variable, which will be used as title. Use title = ""
to remove title.
Character vector of length one or two (depending on the
plot function and type), used as title(s) for the x and y axis. If not
specified, a default labelling is chosen. Note: Some plot types
may not support this argument sufficiently. In such cases, use the returned
ggplot-object and add axis titles manually with
labs. Use axis.title = "" to remove axis
titles.
Character vector with labels for the model terms, used as
axis labels. By default, get_term_labels is
called to retrieve the labels of the coefficients, which will be used as
axis labels. Use axis.labels = "" or auto.label = FALSE to
use the variable names as labels instead. If axis.labels is a named
vector, axis labels (by default, the names of the model's coefficients)
will be matched with the names of axis.label. This ensures that
labels always match the related axis value, no matter in which way
axis labels are sorted.
Character vector, used as legend title for plots that have a legend.
Numeric, determines how many chars of the plot title are displayed in one line and when a line break is inserted.
Numeric, determines how many chars of the value, variable or axis labels are displayed in one line and when a line break is inserted.
Numeric vector of length 2, defining the range of the plot
axis. Depending on plot-type, may effect either x- or y-axis. For
Marginal Effects plots, axis.lim may also be a list of two
vectors of length 2, defining axis limits for both the x and y axis.
Numeric value or vector; if grid.breaks is a
single value, sets the distance between breaks for the axis at every
grid.breaks'th position, where a major grid line is plotted. If
grid.breaks is a vector, values will be used to define the
axis positions of the major grid lines.
Numeric, the level of the confidence intervals (error bars).
Use ci.lvl = NA to remove error bars. For stanreg-models,
ci.lvl defines the (outer) probability for the
hdi (High Density Interval) that is plotted. By
default, stanreg-models are printed with two intervals: the "inner"
interval, which defaults to the 50%-HDI; and the "outer" interval, which
defaults to the 89%-HDI. ci.lvl affects only the outer interval in
such cases. See prob.inner and prob.outer under the
...-argument for more details.
Either a logical, and if TRUE, error bars indicate standard
errors, not confidence intervals. Or a character vector with a specification
of the covariance matrix to compute robust standard errors (see argument
vcov of robust for valid values; robust standard
errors are only supported for models that work with coeftest).
se overrides ci.lvl: if not NULL, arguments ci.lvl
and transform will be ignored. Currently, se only applies
to Coefficients plots.
May be a character vector of color values in hex-format, valid
color value names (see demo("colors")) or a name of a pre-defined
color palette. Following options are valid for the colors argument:
If not specified, a default color brewer palette will be used, which is suitable for the plot style.
If "gs", a greyscale will be used.
If "bw", and plot-type is a line-plot, the plot is black/white and uses different line types to distinguish groups (see this package-vignette).
If colors is any valid color brewer palette name, the related palette will be used. Use display.brewer.all to view all available palette names.
There are some pre-defined color palettes in this package, see sjPlot-themes for details.
Else specify own color values or names as vector (e.g. colors = "#00ff00" or colors = c("firebrick", "blue")).
Logical, if TRUE, the intercept of the fitted
model is also plotted. Default is FALSE. If transform =
"exp", please note that due to exponential transformation of estimates,
the intercept in some cases is non-finite and the plot can not be created.
Logical, whether values should be plotted or not.
Logical, adds asterisks that indicate the significance level of estimates to the value labels.
Logical, for Marginal Effects plots, also plots the raw data points.
For Marginal Effects plots, shows or hides the legend.
Logical, if TRUE, shows the zero-inflation part of
hurdle- or zero-inflated models.
Numeric, offset for text labels to adjust their position relative to the dots or lines.
Numeric, indicates the size of value labels. Can be used
for all plot types where the argument show.values is applicable,
e.g. value.size = 4.
Numeric, between 0 and 1. If show.data = TRUE, you can
add a small amount of random variation to the location of each data point.
jitter then indicates the width, i.e. how much of a bin's width
will be occupied by the jittered values.
Numeric, amount of digits after decimal point when rounding estimates or values.
Numeric, size of the dots that indicate the point estimates.
Numeric, size of the lines that indicate the error bars.
Color of the vertical "zero effect" line. Default color is inherited from the current theme.
Logical, if TRUE, multiple plots are plotted as grid
layout.
Desired target case. Labels will automatically converted into the
specified character case. See to_any_case for more
details on this argument. By default, if case is not specified,
it will be set to "parsed", unless prefix.labels is not
"none". If prefix.labels is either "label" (or
"l") or "varname" (or "v") and case is not
specified, it will be set to NULL - this is a more convenient
default when prefixing labels.
Logical, if TRUE (the default), plot-labels are
based on value and variable labels, if the data is labelled. See
get_label and
get_term_labels for details. If FALSE,
original variable names and value labels (factor levels) are used.
Indicates whether the value labels of categorical variables
should be prefixed, e.g. with the variable name or variable label. See
argument prefix in get_term_labels for
details.
For Stan-models (fitted with the rstanarm- or
brms-package), the Bayesian point estimate is, by default, the median
of the posterior distribution. Use bpe to define other functions to
calculate the Bayesian point estimate. bpe needs to be a character
naming the specific function, which is passed to the fun-argument in
typical_value. So, bpe = "mean" would
calculate the mean value of the posterior distribution.
For Stan-models (fitted with the rstanarm- or
brms-package), the Bayesian point estimate is indicated as a small,
vertical line by default. Use bpe.style = "dot" to plot a dot
instead of a line for the point estimate.
Character vector, indicating the color of the Bayesian
point estimate. Setting bpe.color = NULL will inherit the color
from the mapped aesthetic to match it with the geom's color.
Other arguments, passed down to various functions. Here is a list of supported arguments and their description in detail.
prob.inner and prob.outerFor Stan-models
(fitted with the rstanarm- or brms-package) and coefficients
plot-types, you can specify numeric values between 0 and 1 for
prob.inner and prob.outer, which will then be used as inner
and outer probabilities for the uncertainty intervals (HDI). By default,
the inner probability is 0.5 and the outer probability is 0.89 (unless
ci.lvl is specified - in this case, ci.lvl is used as outer
probability).
size.innerFor Stan-models and Coefficients
plot-types, you can specify the width of the bar for the inner
probabilities. Default is 0.1. Setting size.inner = 0
removes the inner probability regions.
width, alpha, and scalePassed
down to geom_errorbar() or geom_density_ridges(), for
forest or diagnostic plots.
width, alpha, dot.alpha, dodge and log.yPassed
down to plot.ggeffects for Marginal Effects
plots.
show.loessLogical, for diagnostic plot-types "slope"
and "resid", adds (or hides) a loess-smoothed line to the plot.
When plotting marginal effects,
arguments are also passed down to ggpredict,
ggeffect or plot.ggeffects.
For case conversion of labels (see argument
case), arguments sep_in and sep_out will be passed
down to to_any_case. This only
applies to automatically retrieved term labels, not if
term labels are provided by the axis.labels-argument.
Depending on the plot-type, plot_model() returns a
ggplot-object or a list of such objects. get_model_data
returns the associated data with the plot-object as tidy data frame, or
(depending on the plot-type) a list of such data frames.
get_model_data() simply calls plot_model() and returns
the data from the ggplot-object. Hence, it is rather inefficient and should
be used as alternative to brooms tidy()-function only in
specific situations. Some notes on the different plot-types:
type = "std2"Plots standardized beta values, however, standardization follows Gelman's (2008) suggestion, rescaling the estimates by dividing them by two standard deviations instead of just one. Resulting coefficients are then directly comparable for untransformed binary predictors.
type = "pred"Plots marginal effects. Simply wraps
ggpredict.
type = "eff"Plots marginal effects. Simply wraps
ggeffect.
type = "int"A shortcut for marginal effects plots, where
interaction terms are automatically detected and used as
terms-argument. Furthermore, if the moderator variable (the second
- and third - term in an interaction) is continuous, type = "int"
automatically chooses useful values based on the mdrt.values-argument,
which are passed to terms. Then, ggpredict
is called. type = "int" plots the interaction term that appears
first in the formula along the x-axis, while the second (and possibly
third) variable in an interaction is used as grouping factor(s)
(moderating variable). Use type = "pred" or type = "eff"
and specify a certain order in the terms-argument to indicate
which variable(s) should be used as moderator.
Gelman A (2008) "Scaling regression inputs by dividing by two standard deviations." Statistics in Medicine 27: 2865<U+2013>2873. http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf
Aiken and West (1991). Multiple Regression: Testing and Interpreting Interactions.
# NOT RUN {
# prepare data
library(sjmisc)
data(efc)
efc <- to_factor(efc, c161sex, e42dep, c172code)
m <- lm(neg_c_7 ~ pos_v_4 + c12hour + e42dep + c172code, data = efc)
# simple forest plot
plot_model(m)
# grouped coefficients
plot_model(m, group.terms = c(1, 2, 3, 3, 3, 4, 4))
# keep only selected terms in the model: pos_v_4, the
# levels 3 and 4 of factor e42dep and levels 2 and 3 for c172code
plot_model(m, terms = c("pos_v_4", "e42dep [3,4]", "c172code [2,3]"))
# multiple plots, as returned from "diagnostic"-plot type,
# can be arranged with 'plot_grid()'
# }
# NOT RUN {
p <- plot_model(m, type = "diag")
plot_grid(p)
# }
# NOT RUN {
# plot random effects
library(lme4)
m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
plot_model(m, type = "re")
# plot marginal effects
plot_model(m, type = "eff", terms = "Days")
# plot interactions
# }
# NOT RUN {
m <- glm(
tot_sc_e ~ c161sex + c172code * neg_c_7,
data = efc,
family = poisson()
)
# type = "int" automatically selects groups for continuous moderator
# variables - see argument 'mdrt.values'. The following function call is
# identical to:
# plot_model(m, type = "pred", terms = c("c172code", "neg_c_7 [7,28]"))
plot_model(m, type = "int")
# switch moderator
plot_model(m, type = "pred", terms = c("neg_c_7", "c172code"))
# same as
# ggeffects::ggpredict(m, terms = c("neg_c_7", "c172code"))
# }
# NOT RUN {
# plot Stan-model
# }
# NOT RUN {
if (require("rstanarm")) {
data(mtcars)
m <- stan_glm(mpg ~ wt + am + cyl + gear, data = mtcars, chains = 1)
plot_model(m, bpe.style = "dot")
}
# }
# NOT RUN {
# }
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