Plot and compare regression coefficients with confidence intervals of multiple regression models in one plot.

plot_models(..., transform, std.est = NULL, rm.terms = NULL, title = NULL,
  m.labels = NULL, legend.title = "Dependent Variables",
  legend.pval.title = "p-level", axis.labels = NULL, axis.title = NULL,
  axis.lim = NULL, wrap.title = 50, wrap.labels = 25,
  wrap.legend.title = 20, grid.breaks = NULL, dot.size = 3,
  spacing = 0.4, colors = "Set1", show.values = FALSE,
  show.legend = TRUE, show.intercept = FALSE, show.p = TRUE,
  p.shape = FALSE, ci.lvl = 0.95, vline.color = NULL, digits = 2,
  grid = FALSE, auto.label = TRUE, prefix.labels = c("none", "varname",



One or more regression models, including glm's or mixed models. May also be a list with fitted models. See 'Examples'.


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.


For linear models, choose whether standardized coefficients should be used for plotting. Default is no standardization.


(default) no standardization, returns original estimates.


standardized beta values.


standardized beta values, however, standardization is done by rescaling estimates by dividing them by two sd (see std_beta).


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. Note that this argument does not apply to Marginal Effects plots.


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, used to indicate the different models in the plot's legend. If not specified, the labels of the dependent variables for each model are used.


Character vector, used as title for the plot legend. Note that only some plot types have legends (e.g. type = "pred" or when grouping estimates with group.estimates).


Character vector, used as title of the plot legend that indicates the p-values. Default is "p-level". Only applies if p.shape = TRUE.


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 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.


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, 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, determines how many chars of the legend's title are displayed in one line and when a line break is inserted.


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, size of the dots that indicate the point estimates.


Numeric, spacing between the dots and error bars of the plotted fitted models. Default is 0.3.


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.

  • If wesanderson is installed, you may also specify a name of a palette from that package.

  • If viridis is installed, use colors = "v" to get the viridis color palette.

  • 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, whether values should be plotted or not.


For Marginal Effects plots, shows or hides the legend.


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, adds asterisks that indicate the significance level of estimates to the value labels.


Logical, if TRUE, significant levels are distinguished by different point shapes and a related legend is plotted. Default is FALSE.


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.


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


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


Logical, if TRUE, multiple plots are plotted as grid layout.


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.


A ggplot-object.


data(efc) # fit three models fit1 <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc) fit2 <- lm(neg_c_7 ~ c160age + c12hour + c161sex + c172code, data = efc) fit3 <- lm(tot_sc_e ~ c160age + c12hour + c161sex + c172code, data = efc) # plot multiple models plot_models(fit1, fit2, fit3, grid = TRUE)
# plot multiple models with legend labels and # point shapes instead of value labels plot_models( fit1, fit2, fit3, axis.labels = c( "Carer's Age", "Hours of Care", "Carer's Sex", "Educational Status" ), m.labels = c("Barthel Index", "Negative Impact", "Services used"), show.values = FALSE, show.p = FALSE, p.shape = TRUE )
# plot multiple models from nested lists argument all.models <- list() all.models[[1]] <- fit1 all.models[[2]] <- fit2 all.models[[3]] <- fit3 plot_models(all.models)
# plot multiple models with different predictors (stepwise inclusion), # standardized estimates fit1 <- lm(mpg ~ wt + cyl + disp + gear, data = mtcars) fit2 <- update(fit1, . ~ . + hp) fit3 <- update(fit2, . ~ . + am) plot_models(fit1, fit2, fit3, std.est = "std2")