Model Summary Plots with Estimates and Confidence Intervals
modelplot( models, conf_level = 0.95, coef_map = NULL, coef_omit = NULL, coef_rename = NULL, vcov = NULL, add_rows = NULL, facet = FALSE, draw = TRUE, background = NULL, ... )
models  a model or (optionally named) list of models 

conf_level  confidence level to use for confidence intervals 
coef_map  character vector. Subset, rename, and reorder coefficients.
Coefficients omitted from this vector are omitted from the table. The order
of the vector determines the order of the table. 
coef_omit  string regular expression. Omits all matching coefficients
from the table using 
coef_rename  named character vector or function which returns a named
vector. Values of the vector refer to the variable names that will appear
in the table. Names refer to the original term names stored in the model
object, e.g. c("hp:mpg"="hp X mpg") for an interaction term.
If you provide a function to 
vcov  robust standard errors and other manual statistics. The

add_rows  a data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a "position" attribute of integers to set the row positions. See Examples section below. 
facet  TRUE or FALSE. When the 'models' argument includes several model objects, TRUE draws terms in separate facets, and FALSE draws terms sidebyside (dodged). 
draw  TRUE returns a 'ggplot2' object, FALSE returns the data.frame used to draw the plot. 
background  A list of 'ggplot2' geoms to add to the background of the plot. This is especially useful to display annotations "behind" the 'geom_pointrange' that 'modelplot' draws. 
...  all other arguments are passed through to the extractor and
tablemaking functions. This allows users to pass arguments directly to

if (FALSE) { library(modelsummary) # single model mod < lm(hp ~ vs + drat, mtcars) modelplot(mod) # omit terms with string matches or regexes modelplot(mod, coef_omit = 'Interc') # rename, reorder and subset with 'coef_map' cm < c('vs' = 'Vshape engine', 'drat' = 'Rear axle ratio') modelplot(mod, coef_map = cm) # several models models < list() models[['Small model']] < lm(hp ~ vs, mtcars) models[['Medium model']] < lm(hp ~ vs + factor(cyl), mtcars) models[['Large model']] < lm(hp ~ vs + drat + factor(cyl), mtcars) modelplot(models) # add_rows: add an empty reference category mod < lm(hp ~ factor(cyl), mtcars) add_rows = data.frame( term = "factory(cyl)4", model = "Model 1", estimate = NA) attr(add_rows, "position") = 3 modelplot(mod, add_rows = add_rows) # customize your plots with 'ggplot2' functions library(ggplot2) modelplot(models) + scale_color_brewer(type = 'qual') + theme_classic() # pass arguments to 'geom_pointrange' through the ... ellipsis modelplot(mod, color = 'red', size = 1, fatten = .5) # add geoms to the background, behind geom_pointrange b < list(geom_vline(xintercept = 0, color = 'orange'), annotate("rect", alpha = .1, xmin = .5, xmax = .5, ymin = Inf, ymax = Inf), geom_point(aes(y = term, x = estimate), alpha = .3, size = 10, color = 'red', shape = 'square')) modelplot(mod, background = b) }