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 = "plevel", 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", "label"))
...  One or more regression models, including glm's or mixed models.
May also be a 

transform  A character vector, naming a function that will be applied
on estimates and confidence intervals. By default, 
std.est  For linear models, choose whether standardized coefficients should be used for plotting. Default is no standardization.

rm.terms  Character vector with names that indicate which terms should
be removed from the plot. Counterpart to 
title  Character vector, used as plot title. By default,

m.labels  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. 
legend.title  Character vector, used as title for the plot legend. Note that
only some plot types have legends (e.g. 
legend.pval.title  Character vector, used as title of the plot legend that
indicates the pvalues. Default is 
axis.labels  Character vector with labels for the model terms, used as
axis labels. By default, 
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
ggplotobject and add axis titles manually with

axis.lim  Numeric vector of length 2, defining the range of the plot
axis. Depending on plottype, may effect either x or yaxis. For
Marginal Effects plots, 
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. 
wrap.legend.title  numeric, determines how many chars of the legend's title are displayed in one line and when a line break is inserted. 
grid.breaks  Numeric value or vector; if 
dot.size  Numeric, size of the dots that indicate the point estimates. 
spacing  Numeric, spacing between the dots and error bars of the plotted fitted models. Default is 0.3. 
colors  May be a character vector of color values in hexformat, valid
color value names (see

show.values  Logical, whether values should be plotted or not. 
show.legend  For Marginal Effects plots, shows or hides the legend. 
show.intercept  Logical, if 
show.p  Logical, adds asterisks that indicate the significance level of estimates to the value labels. 
p.shape  Logical, if 
ci.lvl  Numeric, the level of the confidence intervals (error bars).
Use 
vline.color  Color of the vertical "zero effect" line. Default color is inherited from the current theme. 
digits  Numeric, amount of digits after decimal point when rounding estimates or values. 
grid  Logical, if 
auto.label  Logical, if 
prefix.labels  Indicates whether the value labels of categorical variables
should be prefixed, e.g. with the variable name or variable label. See
argument 
A ggplotobject.
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")