Graphs have long been known to be a more compact and effective means
of conveying the results of regression models than tables (Gelman, Pasarica, and Dodhia 2002; Kastellec and Leoni
2007), but many researchers continue to list these results in
tables. The reason, Kastellec and Leoni
(2007) surmised, is “simply put, it takes much greater effort to
produce a quality graph than a table.” The `dotwhisker`

package provides a quick and easy way to create highly customizable
dot-and-whisker plots for presenting and comparing the output of
regression models. It can be used to plot estimates of coefficients or
other quantities of interest (e.g., predicted probabilities) within a
single model or across different models: the estimates are presented as
dots and their confidence intervals as whiskers (see Kastellec and Leoni 2007, 765–67).

Users can easily customize the content of their plots: presenting
multiple models or results for a subset of variables is easy. Moreover,
by outputting `ggplot`

objects (Wickham 2009), `dotwhisker`

allows
users to further modify the format of their plots in nearly infinite
ways.

This vignette illustrates basic use of the package’s mainstay
function, `dwplot`

, for creating dot-and-whisker plots from
model objects; more advanced uses of `dwplot`

that employ
tidy data frames as input; and, finally, some useful variations of
dot-and-whisker plots that are easily made using other functions in the
`dotwhisker`

package.

Generating dot-and-whisker plots from model objects generated by the
most commonly used regression functions is straightforward. To make a
basic dot-and-whisker plot of any single model object of a class
supported by `parameters::parameters`

,
simply pass it to `dwplot`

. For these examples, we’ll use the
`mtcars`

dataset extracted from the 1974 volume of the US
magazine, *Motor Trend*.

```
#Package preload
library(dotwhisker)
library(dplyr)
# run a regression compatible with tidy
m1 <- lm(mpg ~ wt + cyl + disp + gear, data = mtcars)
# draw a dot-and-whisker plot
dwplot(m1)
```

By default, the whiskers span the 95% confidence interval. To change
the width of the confidence interval, specify a `ci`

argument
to pass to `parameters::parameters()`

:

Plotting the results of more than one regression model is just as
easy. Just pass the model objects to `dwplot`

as a list. The
`dodge_size`

argument is used to adjust the space between the
estimates of one variable when multiple models are presented in a single
plot. Its default value of .4 will usually be fine, but, depending on
the dimensions of the desired plot, more pleasing results may be
achieved by setting `dodge_size`

to lower values when the
plotted results include a relatively small number of predictors or to
higher values when many models appear on the same plot.

```
m2 <- update(m1, . ~ . + hp) # add another predictor
m3 <- update(m2, . ~ . + am) # and another
dwplot(list(m1, m2, m3))
```

Model intercepts are rarely theoretically interesting (see Kastellec and Leoni 2007, 765), so they are
excluded by `dwplot`

by default. They are easy to include if
desired, however, by setting the `show_intercept`

argument to
true.

Users are free to customize the order of the models and variables to
present with the arguments `model_order`

and
`vars_order`

. Moreover, the output of `dwplot`

is
a `ggplot`

object. Add or change any `ggplot`

layers after calling `dwplot`

to achieve the desired
presentation. Users can provide a named character vector to
`relabel_predictors`

, a `dotwhisker`

function,
conveniently renames the predictors. Note that both
`vars_order`

and `relabel_predictors`

changes the
presenting order of variables. When both are used, the later overwrites
the former.

```
dwplot(list(m1, m2, m3),
vline = geom_vline(
xintercept = 0,
colour = "grey60",
linetype = 2
),
vars_order = c("am", "cyl", "disp", "gear", "hp", "wt"),
model_order = c("Model 2", "Model 1", "Model 3")
) |> # plot line at zero _behind_coefs
relabel_predictors(
c(
am = "Manual",
cyl = "Cylinders",
disp = "Displacement",
wt = "Weight",
gear = "Gears",
hp = "Horsepower"
)
) +
theme_bw(base_size = 4) +
# Setting `base_size` for fit the theme
# No need to set `base_size` in most usage
xlab("Coefficient Estimate") + ylab("") +
geom_vline(xintercept = 0,
colour = "grey60",
linetype = 2) +
ggtitle("Predicting Gas Mileage") +
theme(
plot.title = element_text(face = "bold"),
legend.position = c(0.007, 0.01),
legend.justification = c(0, 0),
legend.background = element_rect(colour = "grey80"),
legend.title = element_blank()
)
```

There are many other packages (e.g., `coefplot`

) that have
the ability to draw dot-and-whisker plots of at least a single set of
regression results taking model objects as input. While this is very
convenient, it also comes with some severe limitations. First, many less
common model objects are not supported. Second, rescaling coefficients,
reordering them, or just plotting a subset of results is typically
impossible. And third, quantities of interest beyond coefficient
estimates cannot be plotted. The `dotwhisker`

package avoids
all of these limitations by optionally taking as its input a tidy data
frame of estimates drawn from a model object rather than the model
object itself.

In addition to model objects, the input for `dwplot`

may
be a tidy data frame that includes three columns: `term`

,
that is, the variable name; `estimate`

, the regression
coefficients or other quantity of interest; and `std.error`

,
the standard errors associated with these estimates. In place of
`std.error`

one may substitute `conf.low`

, the
lower bounds of the confidence intervals of the estimates, and
`conf.high`

, the corresponding upper bounds. As noted above,
“tidy data” (Robinson 2015) refers such a
data frame of estimates for many common classes of model objects
(indeed, `dwplot`

was written to expect a data.frame with the
columns `term`

, `estimate`

, and
`std.error`

). When more than one model’s results are to be
plotted, an additional column `model`

that identifies the two
models must be added to the data frame (alternate names for this last
column may be specified by using the `model_name`

argument).

```
# regression compatible with tidy
m1_df <- broom::tidy(m1) # create data.frame of regression results
m1_df # a tidy data.frame available for dwplot
```

```
## # A tibble: 5 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 43.5 4.86 8.96 0.00000000142
## 2 wt -3.79 1.08 -3.51 0.00161
## 3 cyl -1.78 0.614 -2.91 0.00722
## 4 disp 0.00694 0.0120 0.578 0.568
## 5 gear -0.490 0.790 -0.621 0.540
```

Using `tidy`

can be helpful when one wishes to omit
certain model estimates from the plot. To illustrate, we drop the
intercept (although this is in fact done by `dwplot`

automatically by default):

```
m1_df <-
broom::tidy(m1) %>% filter(term != "(Intercept)") %>% mutate(model = "Model 1")
m2_df <-
broom::tidy(m2) %>% filter(term != "(Intercept)") %>% mutate(model = "Model 2")
two_models <- rbind(m1_df, m2_df)
dwplot(two_models)
```

You can also filter by regular expressions. This can be helpful, for instance, if a model contains a factor with many levels (e.g., a dummy variable for each country) which you might not want to include in your plot.

```
# Transform cyl to factor variable in the data
m_factor <-
lm(mpg ~ wt + cyl + disp + gear, data = mtcars %>% mutate(cyl = factor(cyl)))
# Remove all model estimates that start with cyl*
m_factor_df <- broom::tidy(m_factor) %>%
filter(!grepl('cyl*', term))
dwplot(m_factor_df)
```

It can also be convenient to build a tidy data frame of regression results directly, that is, without first creating model objects:

```
# Run model on subsets of data, save results as tidy df, make a model variable, and relabel predictors
by_trans <- mtcars %>%
group_by(am) %>% # group data by trans
do(broom::tidy(lm(mpg ~ wt + cyl + disp + gear, data = .))) %>% # run model on each grp
rename(model = am) %>% # make model variable
relabel_predictors(c(
wt = "Weight",
# relabel predictors
cyl = "Cylinders",
disp = "Displacement",
gear = "Gear"
))
by_trans
```

```
## # A tibble: 10 × 6
## # Groups: model [2]
## model term estimate std.error statistic p.value
## <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 0 Weight -2.81 1.27 -2.22 0.0434
## 2 0 Cylinders -1.30 0.599 -2.17 0.0473
## 3 0 Displacement 0.00692 0.0129 0.534 0.601
## 4 0 Gear 1.26 1.81 0.696 0.498
## 5 0 (Intercept) 30.7 7.41 4.15 0.000986
## 6 1 Weight -7.53 2.77 -2.71 0.0265
## 7 1 Cylinders 0.198 1.70 0.116 0.910
## 8 1 Displacement -0.0146 0.0315 -0.464 0.655
## 9 1 Gear -1.08 2.14 -0.506 0.627
## 10 1 (Intercept) 48.4 11.1 4.34 0.00247
```

```
dwplot(by_trans,
vline = geom_vline(
xintercept = 0,
colour = "grey60",
linetype = 2
)) + # plot line at zero _behind_ coefs
theme_bw(base_size = 4) + xlab("Coefficient Estimate") + ylab("") +
ggtitle("Predicting Gas Mileage by Transmission Type") +
theme(
plot.title = element_text(face = "bold"),
legend.position = c(0.007, 0.01),
legend.justification = c(0, 0),
legend.background = element_rect(colour = "grey80"),
legend.title.align = .5
) +
scale_colour_grey(
start = .3,
end = .7,
name = "Transmission",
breaks = c(0, 1),
labels = c("Automatic", "Manual")
)
```

Also note in the above example the additional manner of using the
`relabel_predictors`

function: in addition to being used on
the `ggplot`

object created by `dwplot`

before
further customization, it may also be used on a tidy data frame before
it is passed to `dwplot`

.

Additionally, one can change the shape of the point estimate instead of using different colors. This can be useful, for example, when a plot needs to be printed in black and white. Here we also vary the linetype of the whiskers.

```
dwplot(
by_trans,
vline = geom_vline(
xintercept = 0,
colour = "grey60",
linetype = 2
),
# plot line at zero _behind_ coefs
dot_args = list(aes(shape = model)),
whisker_args = list(aes(linetype = model))
) +
theme_bw(base_size = 4) + xlab("Coefficient Estimate") + ylab("") +
ggtitle("Predicting Gas Mileage by Transmission Type") +
theme(
plot.title = element_text(face = "bold"),
legend.position = c(0.007, 0.01),
legend.justification = c(0, 0),
legend.background = element_rect(colour = "grey80"),
legend.title.align = .5
) +
scale_colour_grey(
start = .1,
end = .1,
# if start and end same value, use same colour for all models
name = "Model",
breaks = c(0, 1),
labels = c("Automatic", "Manual")
) +
scale_shape_discrete(
name = "Model",
breaks = c(0, 1),
labels = c("Automatic", "Manual")
) +
guides(
shape = guide_legend("Model"),
colour = guide_legend("Model")
) # Combine the legends for shape and color
```

It is also easy to plot classes of model objects that are not
supported by `tidy`

or `parameters::parameters`

:
one simply extracts the results from the model object and builds the
data frame to pass to `dwplot`

oneself. Many functions
generate results that can be extracted by `coef()`

.

```
# the ordinal regression model is not supported by tidy
m4 <- ordinal::clm(factor(gear) ~ wt + cyl + disp, data = mtcars)
m4_df <- coef(summary(m4)) %>%
data.frame() %>%
tibble::rownames_to_column("term") %>%
rename(estimate = Estimate, std.error = Std..Error)
m4_df
```

```
## term estimate std.error z.value Pr...z..
## 1 3|4 -4.03517968 2.45869171 -1.6411898 0.1007580
## 2 4|5 -1.37662018 2.28622404 -0.6021370 0.5470829
## 3 wt -1.13452561 0.98498075 -1.1518252 0.2493929
## 4 cyl 0.41701081 0.60620009 0.6879095 0.4915098
## 5 disp -0.01343896 0.01188167 -1.1310664 0.2580271
```

Working with a tidy data frame, it is similarly straightforward to
plot just a subset of results or to rescale or reorder coefficients. One
often desirable manipulation is to standardize the scales of variables.
Gelman (2008), for example, suggests
rescaling ordinal and continuous predictors by two standard deviations
to facilitate comparison with dichotomous predictors. Although this can
of course be done before model estimation, it can be more convenient to
simply rescale the coefficients afterwards; the `by_2sd`

function, which takes as arguments a data frame of estimates along with
the original data frame upon which the model was based, automates this
calculation.

```
# Customize the input data frame
m1_df_mod <-
m1_df %>% # the original tidy data.frame
by_2sd(mtcars) %>% # rescale the coefficients
arrange(term) # alphabetize the variables
m1_df_mod # rescaled, with variables reordered alphabetically
```

```
## # A tibble: 4 × 7
## term estimate std.error statistic p.value model by_2sd
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <lgl>
## 1 cyl -6.37 2.19 -2.91 0.00722 Model 1 TRUE
## 2 disp 1.72 2.98 0.578 0.568 Model 1 TRUE
## 3 gear -0.724 1.17 -0.621 0.540 Model 1 TRUE
## 4 wt -7.42 2.12 -3.51 0.00161 Model 1 TRUE
```

It is frequently desirable to convey that the predictors in a model
depicted in a dot-and-whisker plot form groups of some sort. This can be
achieved by passing the finalized plot to the `add_brackets`

function. To pass the finalized plot to `add_brackets`

without creating an intermediate object, simply wrap the code that
generates it in braces (`{`

and `}`

):

```
# Create list of brackets (label, topmost included predictor, bottommost included predictor)
three_brackets <- list(
c("Overall", "Weight", "Weight"),
c("Engine", "Cylinders", "Horsepower"),
c("Transmission", "Gears", "Manual")
)
{
dwplot(list(m1, m2, m3),
vline = geom_vline(
xintercept = 0,
colour = "grey60",
linetype = 2
)) %>% # plot line at zero _behind_ coefs
relabel_predictors(
c(
wt = "Weight",
# relabel predictors
cyl = "Cylinders",
disp = "Displacement",
hp = "Horsepower",
gear = "Gears",
am = "Manual"
)
) + xlab("Coefficient Estimate") + ylab("") +
ggtitle("Predicting Gas Mileage") +
theme(
plot.title = element_text(face = "bold"),
legend.position = c(0.993, 0.99),
legend.justification = c(1, 1),
legend.background = element_rect(colour = "grey80"),
legend.title = element_blank()
)
} %>%
add_brackets(three_brackets, fontSize = 0.3)
```

Inspired
by the way Edwards, Jacobs, and Forrest
(2016, 5) displayed regression coefficients as normal
distributions, `dotwhisker`

now provides an easy way to make
similar plots. To create such plots, call `dwplot`

as always
but include the argument `style = "distribution"`

, then
customize with other `dotwhisker`

functions and
`ggplot`

additions as usual:

```
by_transmission_brackets <- list(
c("Overall", "Weight", "Weight"),
c("Engine", "Cylinders", "Horsepower"),
c("Transmission", "Gears", "1/4 Mile/t")
)
{
mtcars %>%
split(.$am) %>%
purrr::map( ~ lm(mpg ~ wt + cyl + gear + qsec, data = .x)) %>%
dwplot(style = "distribution") %>%
relabel_predictors(
wt = "Weight",
cyl = "Cylinders",
disp = "Displacement",
hp = "Horsepower",
gear = "Gears",
qsec = "1/4 Mile/t"
) +
theme_bw(base_size = 4) + xlab("Coefficient") + ylab("") +
geom_vline(xintercept = 0,
colour = "grey60",
linetype = 2) +
theme(
legend.position = c(.995, .99),
legend.justification = c(1, 1),
legend.background = element_rect(colour = "grey80"),
legend.title.align = .5
) +
scale_colour_grey(
start = .8,
end = .4,
name = "Transmission",
breaks = c("Model 0", "Model 1"),
labels = c("Automatic", "Manual")
) +
scale_fill_grey(
start = .8,
end = .4,
name = "Transmission",
breaks = c("Model 0", "Model 1"),
labels = c("Automatic", "Manual")
) +
ggtitle("Predicting Gas Mileage by Transmission Type") +
theme(plot.title = element_text(face = "bold", hjust = 0.5))
} %>%
add_brackets(by_transmission_brackets, fontSize = 0.3)
```

A variation of dot-and-whisker plot is used to compare the estimated
coefficients for a single predictor across many models or datasets:
Andrew Gelman calls such plots the
‘secret weapon’. They are easy to make with the
`secret_weapon`

function. Like `dwplot`

, the
function accepts both lists of model objects and tidy data frames as
input. The `var`

argument is used to specify the predictor
for which results are to be plotted.

```
data(diamonds)
# Estimate models for many subsets of data, put results in a tidy data.frame
by_clarity <- diamonds %>%
group_by(clarity) %>%
do(broom::tidy(lm(price ~ carat + cut + color, data = .), conf.int = .99)) %>%
ungroup %>% rename(model = clarity)
# Deploy the secret weapon
secret_weapon(by_clarity, var = "carat") +
xlab("Estimated Coefficient (Dollars)") + ylab("Diamond Clarity") +
ggtitle("Estimates for Diamond Size Across Clarity Grades") +
theme(plot.title = element_text(face = "bold"))
```

A final means of presenting many models’ results at once in a
particularly compact format is the “small multiple” plot of regression
results (see Kastellec and Leoni 2007,
766). Small-multiple plots present estimates in multiple panels,
one for each variable: they are similar to a stack of secret weapon
plots. The `small_multiple`

function makes generating these
plots simple. Here, we pass a tidy data frame of six models to the
function so we can to rescale the coefficients first, but the function
can accept a list of model objects as well.

```
# Generate a tidy data frame of regression results from six models
m <- list()
ordered_vars <- c("wt", "cyl", "disp", "hp", "gear", "am")
m[[1]] <- lm(mpg ~ wt, data = mtcars)
m123456_df <- m[[1]] %>%
broom::tidy() %>%
by_2sd(mtcars) %>%
mutate(model = "Model 1")
for (i in 2:6) {
m[[i]] <- update(m[[i - 1]], paste(". ~ . +", ordered_vars[i]))
m123456_df <- rbind(m123456_df,
m[[i]] %>%
broom::tidy() %>%
by_2sd(mtcars) %>%
mutate(model = paste("Model", i)))
}
# Relabel predictors (they will appear as facet labels)
m123456_df <- m123456_df %>%
relabel_predictors(
c(
"(Intercept)" = "Intercept",
wt = "Weight",
cyl = "Cylinders",
disp = "Displacement",
hp = "Horsepower",
gear = "Gears",
am = "Manual"
)
)
# Generate a 'small multiple' plot
small_multiple(m123456_df) +
theme_bw(base_size = 4) + ylab("Coefficient Estimate") +
geom_hline(yintercept = 0,
colour = "grey60",
linetype = 2) +
ggtitle("Predicting Mileage") +
theme(
plot.title = element_text(face = "bold"),
legend.position = "none",
axis.text.x = element_text(angle = 60, hjust = 1)
)
```

To facilitate comparisons across, e.g., results generated across
different samples, one can cluster the results presented in a small
multiple plot. To do so, results that should be clustered should have
the same value of `model`

, but should be assigned different
values of an additional `submodel`

variable included in the
tidy data frame passed to `small_multiple`

. (We also
replicate three examples in Kastellec and Leoni
(2007) with `dotwhisker`

in a separate vignette,
“kl2007_examples”.)

```
# Generate a tidy data frame of regression results from five models on
# the mtcars data subset by transmission type
ordered_vars <- c("wt", "cyl", "disp", "hp", "gear")
mod <- "mpg ~ wt"
by_trans2 <- mtcars %>%
group_by(am) %>% # group data by transmission
do(broom::tidy(lm(mod, data = .))) %>% # run model on each group
rename(submodel = am) %>% # make submodel variable
mutate(model = "Model 1") %>% # make model variable
ungroup()
for (i in 2:5) {
mod <- paste(mod, "+", ordered_vars[i])
by_trans2 <- rbind(
by_trans2,
mtcars %>%
group_by(am) %>%
do(broom::tidy(lm(mod, data = .))) %>%
rename(submodel = am) %>%
mutate(model = paste("Model", i)) %>%
ungroup()
)
}
# Relabel predictors (they will appear as facet labels)
by_trans2 <- by_trans2 %>%
select(-submodel, everything(), submodel) %>%
relabel_predictors(
c(
"(Intercept)" = "Intercept",
wt = "Weight",
cyl = "Cylinders",
disp = "Displacement",
hp = "Horsepower",
gear = "Gears"
)
)
by_trans2
```

```
## # A tibble: 40 × 7
## term estimate std.error statistic p.value model submodel
## <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Intercept 31.4 2.95 10.7 6.01e- 9 Model 1 0
## 2 Intercept 46.3 3.12 14.8 1.28e- 8 Model 1 1
## 3 Weight -3.79 0.767 -4.94 1.25e- 4 Model 1 0
## 4 Weight -9.08 1.26 -7.23 1.69e- 5 Model 1 1
## 5 Intercept 34.6 2.48 13.9 2.31e-10 Model 2 0
## 6 Intercept 46.2 3.17 14.6 4.51e- 8 Model 2 1
## 7 Weight -2.23 0.752 -2.96 9.19e- 3 Model 2 0
## 8 Weight -7.40 2.40 -3.09 1.15e- 2 Model 2 1
## 9 Cylinders -1.30 0.379 -3.43 3.43e- 3 Model 2 0
## 10 Cylinders -0.789 0.953 -0.828 4.27e- 1 Model 2 1
## # ℹ 30 more rows
```

```
small_multiple(by_trans2) +
theme_bw(base_size = 4) +
ylab("Coefficient Estimate") +
geom_hline(yintercept = 0,
colour = "grey60",
linetype = 2) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = c(0.02, 0.008),
legend.justification = c(0, 0),
legend.title = element_text(size = 8),
legend.background = element_rect(color = "gray90"),
legend.spacing = unit(-4, "pt"),
legend.key.size = unit(10, "pt")
) +
scale_colour_hue(
name = "Transmission",
breaks = c(0, 1),
labels = c("Automatic", "Manual")
) +
ggtitle("Predicting Gas Mileage\nby Transmission Type")
```

When presenting visualized outputs, users often receive comments that the plots cannot convey all the information a regression table contains, such as the model fits. Upon such comments, there are usually two types of solution. One is to present the regression tables in an appendix, despite the primary information has already been given by the dot-whisker plots. The second option is to add the statistics manually into the plots as an annotation (e.g., Kastellec and Leoni 2007, fig. 6). To make the application of the second solution more user friendly and minimize the need for lengthy, distracting regression tables (even relegated into an appendix), we provide an option to incorporates model fit statistics directly beneath the dot-whisker plots.

Thanks for the functiona from the excellent package
`performance`

, the model fits can be easily extracted into an
`data.frame`

. Based on `performance`

, together
with another life-saving package for ggplot visualization,
`patchwork`

, we offer the `show_stats`

argument in
`dwplot`

and `small_multiple`

. When the argument
is set to be `TRUE`

, users will see the model fits appearing
below the dot-whisker plot:

For added flexibility, we also include a suit of `stats_*`

functions for users’ fine-tuning the presentation of these statistics.
Furthermore, we offer the `stats_tb`

argument, by which users
can input customized fitness information as a
`data.frame`

.

Here are some tips for using this advanced function:

- Different model types often yield varied model fit statistics. By
default,
`dotwhisker`

displays all these statistics collectively. However, this can lead to an excessively wide bin beneath the plot. To address this, users have the option to set`stats_compare`

to`TRUE`

. Activating this argument ensures that only the shared, comparable metrics are retained, streamlining the presentation.

Since the model fits needs to be calculated with the raw model outputs, the model fits will not be able to presented with customized model outputs (i.e., a

`data.frame`

or`tibble`

). Users have to give the customized model fit statistics, as well. It is an easy job, though, with our hidden`dw_stats`

function, if the original models are compatible with the`performance`

package`dotwhisker`

requires raw model outputs to calculate model fit statistics. Consequently, when using customized model outputs (like a`data.frame`

or`tibble`

), the automatic generation of model fit statistics is not feasible. In such cases, users must provide the customized model fit statistics separately. Fortunately, this task can be simplified by the hidden`dw_stats`

function of`dotwhisker`

. This function is particularly efficient if the original models are compatible with the`performance`

package, facilitating seamless integration of model fit statistics.

```
stats_fakeCustom <-
dotwhisker:::dw_stats(m1, stats_digits = 2, stats_compare = FALSE)
dwplot(
m1_df,
show_stats = TRUE,
stats_tb = stats_fakeCustom,
stats_size = 3
)
```

- Currently, the function for displaying model fit is not directly
compatible with the
`relabel_predictors`

and`add_brackets`

functions. However, there is a workaround for integrating these features. The example below illustrates this method. It involves separately generating the model fits and then manually merging them with the bracket-enhanced dotwhisker plot.

```
library(gridExtra)
library(patchwork)
three_brackets <- list(
c("Overall", "Weight", "Weight"),
c("Engine", "Cylinders", "Horsepower"),
c("Transmission", "Gears", "Manual")
)
plot_brackets <- {
dwplot(m3,
vline = geom_vline(
xintercept = 0,
colour = "grey60",
linetype = 2
)) %>% # plot line at zero _behind_ coefs
relabel_predictors(
c(
wt = "Weight",
# relabel predictors
cyl = "Cylinders",
disp = "Displacement",
hp = "Horsepower",
gear = "Gears",
am = "Manual"
)
) + xlab("Coefficient Estimate") + ylab("") +
ggtitle("Predicting Gas Mileage")
} %>%
add_brackets(three_brackets, fontSize = 0.3)
plot_brackets / tableGrob(
dotwhisker:::dw_stats(
m3,
stats_digits = 2,
stats_compare = FALSE
),
rows = NULL,
theme = ttheme_default(base_size = 3)
) +
plot_layout(heights = c(5, -1.5, 1)) # the negative value is used to adjust the space between the plot and the model fits
```

The `dotwhisker`

package provides a flexible and
convenient way to visualize regression results and to compare them
across models. This vignette offers an overview of its use and features.
We encourage users to consult the help files for more details.

The development of the package is ongoing. Please contact us with any questions, bug reports, and comments.

Frederick Solt

Department of Political Science,

University of Iowa,

324 Schaeffer Hall,

20 E Washington St, Iowa City, IA, 52242

Email: frederick-solt@uiowa.edu

Website: https://fsolt.org

Yue Hu

Department of Political Science,

Tsinghua University,

Mingzhai 414,

Zhongguancun Avenue, Haidian, Beijing 100084

Email: yuehu@tsinghua.edu.cn

Website: https://www.drhuyue.site

Edwards, Benjamin, Jay Jacobs, and Stephanie Forrest. 2016. “Risky
Business: Assessing Security with External Measurements.”

Gelman, Andrew. 2008. “Scaling Regression Inputs by Dividing by
Two Standard Deviations.” *Statistics in Medicine* 27
(15): 2865–73.

Gelman, Andrew, Cristian Pasarica, and Rahul Dodhia. 2002. “Let’s
Practice What We Preach: Turning Tables into Graphs.”
*American Statistician* 56 (2): 121–30.

Kastellec, Jonathan P., and Eduardo L. Leoni. 2007. “Using Graphs
Instead of Tables in Political Science.” *Perspectives on
Politics* 5 (4): 755–71.

Robinson, David. 2015. *broom: Convert
Statistical Analysis Objects into Tidy Data Frames*. https://CRAN.R-project.org/package=broom.

Wickham, Hadley. 2009. *ggplot2: Elegant
Graphics for Data Analysis*. New York: Springer. https://ggplot2-book.org/.