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,
point.size = 5,
line.size = c(0.8, 2),
legend.title = "Model",
groups = NULL,
facet.rows = NULL,
facet.cols = NULL,
facet.label.pos = "top",
color.class = colors,
resp = NULL,
dpar = NULL,
coefs.match = c("exact", "regex")
)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,
point.size = 5,
line.size = c(0.8, 2),
legend.title = "Model",
groups = NULL,
facet.rows = NULL,
facet.cols = NULL,
facet.label.pos = "top",
color.class = colors,
resp = NULL,
dpar = NULL,
coefs.match = c("exact", "regex")
)
A ggplot object.
regression model(s). You may also include arguments to be passed
to tidy().
The desired width of confidence intervals for the coefficients. Default: 0.95
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
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
If you'd like to specify some coefficients to not include
in the plot, provide them as a vector. This argument is overridden by
coefs if both are provided. By default, the intercept term is omitted.
To include the intercept term, just set omit.coefs to NULL.
Plot a thicker line representing some narrower span
than ci_level. Default is NULL, but good options are .9, .8, or .5.
See jtools_colors for more on your color options. Default: 'CUD Bright'
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.
If plot.distributions is TRUE, the default
behavior is to plot each normal density curve on the same scale. If some
of the uncertainty intervals are much wider/narrower than others, that
means the wide ones will have such a low height that you won't be able
to see the curve. If you set this parameter to TRUE, each curve will
have the same maximum height regardless of their width.
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.
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. You may also pass a vector of shapes to specify shapes yourself.
Change the size of the points. Default is 3.
Change the thickness of the error bar lines.
Default is c(0.8, 2). The first number is the size for the full
width of the interval, the second number is used for the thicker
inner interval when inner.ci is TRUE.
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 ggplot2's typical methods.
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.
The number of rows in the facet grid (the nrow argument
to ggplot2::facet_wrap()).
The number of columns in the facet grid (the nrow
argument to ggplot2::facet_wrap()).
Where to put the facet labels. One of "top" (the default), "bottom", "left", or "right".
Deprecated. Now known as colors.
For any models that are brmsfit and have multiple response
variables, specify them with a vector here. If the model list includes
other types of models, you do not need to enter resp for those models.
For instance, if I want to plot a lm object and two brmsfit objects,
you only need to provide a vector of length 2 for resp.
For any models that are brmsfit and have a distributional
dependent variable, that can be specified here. If NULL, it is assumed you
want coefficients for the location/mean parameter, not the distributional
parameter(s).
This modifies the way the coefs and omit.coefs
arguments are interpreted. The default "exact" which represents the
legacy behavior, will include/exclude coefficients that match exactly
with your inputs to those functions. If "regex", coefs and
omit.coefs are used as the pattern argument for grepl() matching
the coefficient names. Note that using "regex" means you will be unable
to override the default coefficient names via a named vector.
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)
Run the code above in your browser using DataLab