plot_summs | R Documentation |
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")
)
... |
regression model(s). You may also include arguments to be passed
to |
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. You may also pass a vector of shapes to specify shapes yourself. |
point.size |
Change the size of the points. Default is 3. |
line.size |
Change the thickness of the error bar lines.
Default is |
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 |
resp |
For any models that are |
dpar |
For any models that are |
coefs.match |
This modifies the way the |
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"))
A ggplot object.
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)
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