`plot_model()`

creates plots from regression models, either
estimates (as so-called forest or dot whisker plots) or marginal effects.

## Usage

```
plot_model(
model,
type = c("est", "re", "eff", "emm", "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,
robust = FALSE,
vcov.fun = NULL,
vcov.type = NULL,
vcov.args = 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,
p.threshold = c(0.05, 0.01, 0.001),
p.val = NULL,
p.adjust = NULL,
grid,
case,
auto.label = TRUE,
prefix.labels = c("none", "varname", "label"),
bpe = "median",
bpe.style = "line",
bpe.color = "white",
ci.style = c("whisker", "bar"),
...
)
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,
...
)
```

## Arguments

- model
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
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 coefficients.

`type = "std2"`

Forest-plot of standardized coefficients, 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 = "emm"`

Similar to

`type = "eff"`

, see`ggemmeans`

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). See 'Details'.

`type = "resid"`

Slope of coefficients for each single predictor, against the residuals (linear relationship between each model term and residuals). See 'Details'.

`type = "diag"`

Check model assumptions. See 'Details'.

**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.- transform
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.- terms
Character vector with the names of those terms from

`model`

that should be plotted. This argument depends on the plot-type:*Coefficients*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.*Marginal Effects*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, numeric vectors for which no specific values are given, a "pretty range" is calculated, 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. If all values for a numeric vector should be used to compute predictions, you may use e.g. terms = "age [all]". For more details, see`ggpredict`

.

- sort.est
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.

- rm.terms
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.- group.terms
Numeric vector with group indices, to group coefficients. Each group of coefficients gets its own color (see 'Examples').

- order.terms
Numeric vector, indicating in which order the coefficients should be plotted. See examples in this package-vignette.

- pred.type
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`

.- mdrt.values
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.

- ri.nr
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.- title
Character vector, used as plot title. By default,

`response_labels`

is called to retrieve the label of the dependent variable, which will be used as title. Use`title = ""`

to remove title.- axis.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.- axis.labels
Character vector with labels for the model terms, used as axis labels. By default,

`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.- legend.title
Character vector, used as legend title for plots that have a legend.

- wrap.title
Numeric, determines how many chars of the plot title are displayed in one line and when a line break is inserted.

- wrap.labels
Numeric, determines how many chars of the value, variable or axis labels are displayed in one line and when a line break is inserted.

- axis.lim
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.- grid.breaks
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.- ci.lvl
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*credible interval*that is plotted (see`ci`

). By default,`stanreg`

-models are printed with two intervals: the "inner" interval, which defaults to the 50%-CI; and the "outer" interval, which defaults to the 89%-CI.`ci.lvl`

affects only the outer interval in such cases. See`prob.inner`

and`prob.outer`

under the`...`

-argument for more details.- se
Logical, if

`TRUE`

, the standard errors are also printed. If robust standard errors are required, use arguments`vcov.fun`

,`vcov.type`

and`vcov.args`

(see`standard_error`

for details), or use argument`robust`

as shortcut.`se`

overrides`ci.lvl`

: if not`NULL`

, arguments`ci.lvl`

and`transform`

will be ignored. Currently,`se`

only applies to*Coefficients*plots.- robust
Deprecated. Please use

`vcov.fun`

directly to specify the estimation of the variance-covariance matrix.- vcov.fun
Variance-covariance matrix used to compute uncertainty estimates (e.g., for 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

`model_parameters()`

.- vcov.type
Deprecated. The

`type`

-argument is now included in`vcov.args`

.- vcov.args
List of arguments to be passed to the function identified by the

`vcov.fun`

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.- colors
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`RColorBrewer::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")`

).

- show.intercept
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.- show.values
Logical, whether values should be plotted or not.

- show.p
Logical, adds asterisks that indicate the significance level of estimates to the value labels.

- show.data
Logical, for

*Marginal Effects*plots, also plots the raw data points.- show.legend
For

*Marginal Effects*plots, shows or hides the legend.- show.zeroinf
Logical, if

`TRUE`

, shows the zero-inflation part of hurdle- or zero-inflated models.- value.offset
Numeric, offset for text labels to adjust their position relative to the dots or lines.

- value.size
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`

.- jitter
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.- digits
Numeric, amount of digits after decimal point when rounding estimates or values.

- dot.size
Numeric, size of the dots that indicate the point estimates.

- line.size
Numeric, size of the lines that indicate the error bars.

- vline.color
Color of the vertical "zero effect" line. Default color is inherited from the current theme.

- p.threshold
Numeric vector of length 3, indicating the treshold for annotating p-values with asterisks. Only applies if

`p.style = "asterisk"`

.- p.val
Character specifying method to be used to calculate p-values. Defaults to "profile" for glm/polr models, otherwise "wald".

- p.adjust
Character vector, if not

`NULL`

, indicates the method to adjust p-values. See`p.adjust`

for details.- grid
Logical, if

`TRUE`

, multiple plots are plotted as grid layout.- case
Desired target case. Labels will automatically converted into the specified character case. See

`snakecase::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.- auto.label
Logical, if

`TRUE`

(the default), and data is labelled,`term_labels`

is called to retrieve the labels of the coefficients, which will be used as predictor labels. If data is not labelled, format_parameters() is used to create pretty labels. If`auto.label = FALSE`

, original variable names and value labels (factor levels) are used.- prefix.labels
Indicates whether the value labels of categorical variables should be prefixed, e.g. with the variable name or variable label. See argument

`prefix`

in`term_labels`

for details.- bpe
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.- bpe.style
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.- bpe.color
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.- ci.style
Character vector, defining whether inner and outer intervals for Bayesion models are shown in boxplot-style (

`"whisker"`

) or in bars with different alpha-levels (`"bar"`

).- ...
Other arguments, passed down to various functions. Here is a list of supported arguments and their description in detail.

`prob.inner`

and`prob.outer`

For

**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.inner`

For

**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`scale`

Passed down to

`geom_errorbar()`

or`geom_density_ridges()`

, for forest or diagnostic plots.`width`

,`alpha`

,`dot.alpha`

,`dodge`

and`log.y`

Passed down to

`plot.ggeffects`

for*Marginal Effects*plots.`show.loess`

Logical, for diagnostic plot-types

`"slope"`

and`"resid"`

, adds (or hides) a loess-smoothed line to the plot.*Marginal Effects*plot-typesWhen plotting marginal effects, arguments are also passed down to

`ggpredict`

,`ggeffect`

or`plot.ggeffects`

.- Case conversion of labels
For case conversion of labels (see argument

`case`

), arguments`sep_in`

and`sep_out`

will be passed down to`snakecase::to_any_case()`

. This only applies to automatically retrieved term labels,*not*if term labels are provided by the`axis.labels`

-argument.

## Value

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.

## Details

### Different Plot Types

`type = "std"`

Plots standardized estimates. See details below.

`type = "std2"`

Plots standardized estimates, 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 estimated marginal means (or marginal effects). Simply wraps

`ggpredict`

. See also this package-vignette.`type = "eff"`

Plots estimated marginal means (or marginal effects). Simply wraps

`ggeffect`

. See also this package-vignette.`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. See also this package-vignette.`type = "slope"`

and`type = "resid"`

Simple diagnostic-plots, where a linear model for each single predictor is plotted against the response variable, or the model's residuals. Additionally, a loess-smoothed line is added to the plot. The main purpose of these plots is to check whether the relationship between outcome (or residuals) and a predictor is roughly linear or not. Since the plots are based on a simple linear regression with only one model predictor at the moment, the slopes (i.e. coefficients) may differ from the coefficients of the complete model.

`type = "diag"`

For

**Stan-models**, plots the prior versus posterior samples. For**linear (mixed) models**, plots for multicollinearity-check (Variance Inflation Factors), QQ-plots, checks for normal distribution of residuals and homoscedasticity (constant variance of residuals) are shown. For**generalized linear mixed models**, returns the QQ-plot for random effects.

### Standardized Estimates

Default standardization is done by completely refitting the model on the
standardized data. Hence, this approach is equal to standardizing the
variables before fitting the model, which is particularly recommended for
complex models that include interactions or transformations (e.g., polynomial
or spline terms). When `type = "std2"`

, standardization of estimates
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.

## References

Gelman A (2008) "Scaling regression inputs by dividing by two
standard deviations." *Statistics in Medicine 27: 2865-2873.*
http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf

Aiken and West (1991). Multiple Regression: Testing and Interpreting Interactions.