plot_summs
and plot_coefs
create regression coefficient
plots with ggplot2.
plot_summs(..., ci_level = 0.95, model.names = NULL, coefs = NULL, omit.coefs = "(Intercept)", inner_ci_level = NULL, colors = "CUD Bright", plot.distributions = FALSE, rescale.distributions = FALSE, exp = FALSE, point.shape = TRUE, legend.title = "Model", groups = NULL, facet.rows = NULL, facet.cols = NULL, facet.label.pos = "top", color.class = colors) plot_coefs(..., ci_level = 0.95, inner_ci_level = NULL, model.names = NULL, coefs = NULL, omit.coefs = c("(Intercept)", "Intercept"), colors = "CUD Bright", plot.distributions = FALSE, rescale.distributions = FALSE, exp = FALSE, point.shape = TRUE, legend.title = "Model", groups = NULL, facet.rows = NULL, facet.cols = NULL, facet.label.pos = "top", color.class = colors)
...  regression model(s). 

ci_level  The desired width of confidence intervals for the coefficients. Default: 0.95 
model.names  If plotting multiple models simultaneously, you can provide a vector of names here. If NULL, they will be named sequentially as "Model 1", "Model 2", and so on. Default: NULL 
coefs  If you'd like to include only certain coefficients, provide them as a vector. If it is a named vector, then the names will be used in place of the variable names. See details for examples. Default: NULL 
omit.coefs  If you'd like to specify some coefficients to not include
in the plot, provide them as a vector. This argument is overridden by

inner_ci_level  Plot a thicker line representing some narrower span
than 
colors  See jtools_colors for more on your color options. Default: 'CUD Bright' 
plot.distributions  Instead of just plotting the ranges, you may plot normal distributions representing the width of each estimate. Note that these are completely theoretical and not based on a bootstrapping or MCMC procedure, even if the source model was fit that way. Default is FALSE. 
rescale.distributions  If 
exp  If TRUE, all coefficients are exponentiated (e.g., transforms logit coefficents from log odds scale to odds). The reference line is also moved to 1 instead of 0. 
point.shape  When using multiple models, should each model's point estimates use a different point shape to visually differentiate each model from the others? Default is TRUE. 
legend.title  What should the title for the legend be? Default is
"Model", but you can specify it here since it is rather difficult to
change later via 
groups  If you would like to have facets (i.e., separate panes) for different groups of coefficients, you can specify those groups with a list here. See details for more on how to do this. 
facet.rows  The number of rows in the facet grid (the 
facet.cols  The number of columns in the facet grid (the 
facet.label.pos  Where to put the facet labels. One of "top" (the default), "bottom", "left", or "right". 
color.class  Deprecated. Now known as 
A ggplot object.
A note on the distinction between plot_summs
and plot_coefs
:
plot_summs
only accepts models supported by summ()
and allows users
to take advantage of the standardization and robust standard error features
(among others as may be relevant). plot_coefs
supports any models that
have a broom::tidy()
method defined in the broom package, but of course
lacks any additional features like robust standard errors. To get a mix
of the two, you can pass summ
objects to plot_coefs
too.
For coefs
, if you provide a named vector of coefficients, then
the plot will refer to the selected coefficients by the names of the
vector rather than the coefficient names. For instance, if I want to
include only the coefficients for the hp
and mpg
but have
the plot refer to them as "Horsepower" and "Miles/gallon", I'd provide
the argument like this:
c("Horsepower" = "hp", "Miles/gallon" = "mpg")
To use the groups
argument, provide a (preferably named) list of
character vectors. If I want separate panes with "Frost" and "Illiteracy"
in one and "Population" and "Area" in the other, I'd make a list like
this:
list(pane_1 = c("Frost", "Illiteracy"), pane_2 = c("Population", "Area"))
states < as.data.frame(state.x77) fit1 < lm(Income ~ Frost + Illiteracy + Murder + Population + Area + `Life Exp` + `HS Grad`, data = states, weights = runif(50, 0.1, 3)) fit2 < lm(Income ~ Frost + Illiteracy + Murder + Population + Area + `Life Exp` + `HS Grad`, data = states, weights = runif(50, 0.1, 3)) fit3 < lm(Income ~ Frost + Illiteracy + Murder + Population + Area + `Life Exp` + `HS Grad`, data = states, weights = runif(50, 0.1, 3)) # Plot all 3 regressions with custom predictor labels, # standardized coefficients, and robust standard errors plot_summs(fit1, fit2, fit3, coefs = c("Frost Days" = "Frost", "% Illiterate" = "Illiteracy", "Murder Rate" = "Murder"), scale = TRUE, robust = TRUE)