sjPlot (version 2.8.4)

plot_model: Plot regression models


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


  type = c("est", "re", "eff", "emm", "pred", "int", "std", "std2", "slope", "resid",
  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"), = 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, = NULL,
  vcov.type = c("HC3", "const", "HC", "HC0", "HC1", "HC2", "HC4", "HC4m", "HC5"),
  vcov.args = NULL,
  colors = "Set1",
  show.intercept = FALSE,
  show.values = FALSE,
  show.p = TRUE, = FALSE,
  show.legend = TRUE,
  show.zeroinf = TRUE,
  value.offset = NULL,
  jitter = NULL,
  digits = 2,
  dot.size = NULL,
  line.size = NULL,
  vline.color = NULL,
  p.threshold = c(0.05, 0.01, 0.001),
  p.adjust = NULL,
  auto.label = TRUE,
  prefix.labels = c("none", "varname", "label"),
  bpe = "median", = "line",
  bpe.color = "white", = 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"), = 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 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.


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.

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.


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").


(default) minimum and maximum values (lower and upper bounds) of the moderator are used to plot the interaction between independent variable and moderator(s).


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).


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.


calculates and uses the quartiles (lower, median and upper) of the moderator value.


uses all values of the moderator variable.

Numeric vector. If type = "re" and fitted model has more than one random intercept, 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, response_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, 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 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.


Logical, if TRUE, the standard errors are also printed. If robust standard errors are required, use arguments, vcov.type and vcov.args (see standard_error_robust and this vignette 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.


Logical, shortcut for arguments and vcov.type. If TRUE, uses = "vcovHC" and vcov.type = "HC3" as default, that is, vcovHC with default-type is called (see standard_error_robust and this vignette for further details).

Character vector, indicating the name of the vcov*()-function from the sandwich or clubSandwich package, e.g. = "vcovCL", if robust standard errors are required.


Character vector, specifying the estimation type for the robust covariance matrix estimation (see vcovHC() or vcovCR() for details).


List of named vectors, used as additional arguments that are passed down to


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 = 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.


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


Character vector, if not NULL, indicates the method to adjust p-values. See p.adjust for details.


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), 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.


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.


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 = "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.

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).


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.


Logical, for diagnostic plot-types "slope" and "resid", adds (or hides) a loess-smoothed line to the plot.

Marginal Effects plot-types

When 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 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.


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.


Gelman A (2008) "Scaling regression inputs by dividing by two standard deviations." Statistics in Medicine 27: 2865-2873.

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


Run this code
# prepare data
efc <- to_factor(efc, c161sex, e42dep, c172code)
m <- lm(neg_c_7 ~ pos_v_4 + c12hour + e42dep + c172code, data = efc)

# simple forest plot

# 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()'
# }
p <- plot_model(m, type = "diag")
# }
# plot random effects
if (require("lme4")) {
  m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
  plot_model(m, type = "re")

  # plot marginal effects
  plot_model(m, type = "pred", terms = "Days")
# plot interactions
# }
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"))
# }
# plot Stan-model
# }
if (require("rstanarm")) {
  m <- stan_glm(mpg ~ wt + am + cyl + gear, data = mtcars, chains = 1)
  plot_model(m, = "dot")
# }
# }

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