Dot-Whisker plot of coefficient estimates with confidence intervals. For
more information, see the Details and Examples sections below, and the
vignettes on the `modelsummary`

website:
https://vincentarelbundock.github.io/modelsummary/

## Usage

```
modelplot(
models,
conf_level = 0.95,
coef_map = NULL,
coef_omit = NULL,
coef_rename = NULL,
vcov = NULL,
exponentiate = FALSE,
add_rows = NULL,
facet = FALSE,
draw = TRUE,
background = NULL,
...
)
```

## Arguments

- models
a model, (named) list of models, or nested list of models.

Single model:

`modelsummary(model)`

Unnamed list of models:

`modelsummary(list(model1, model2))`

Models are labelled automatically. The default label style can be altered by setting a global option. See below.

Named list of models:

`modelsummary(list("A"=model1, "B"=model2))`

Models are labelled using the list names.

Nested list of models: When using the

`shape="rbind"`

argument,`models`

can be a nested list of models to display "panels" or "stacks" of regression models. See the`shape`

argument documentation and examples below.

- conf_level
numeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to

`NULL`

does not extract confidence intervals, which can be faster for some models.- 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_map`

can be a named or an unnamed character vector. If`coef_map`

is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object:`c("hp:mpg"="HPxM/G")`

. See Examples section below.- coef_omit
integer vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible "negative lookaheads" can be used to specify which coefficients to

*keep*in the table. Examples:c(2, 3, 5): omits the second, third, and fifth coefficients.

c(-2, -3, -5): negative values keep the second, third, and fifth coefficients.

`"ei"`

: omit coefficients matching the "ei" substring.`"^Volume$"`

: omit the "Volume" coefficient.`"ei|rc"`

: omit coefficients matching either the "ei" or the "rc" substrings.`"^(?!Vol)"`

: keep coefficients starting with "Vol" (inverse match using a negative lookahead).`"^(?!.*ei)"`

: keep coefficients matching the "ei" substring.`"^(?!.*ei|.*pt)"`

: keep coefficients matching either the "ei" or the "pt" substrings.See the Examples section below for complete code.

- coef_rename
logical, named or unnamed character vector, or function

Logical: TRUE renames variables based on the "label" attribute of each column. See the Example section below.

Unnamed character vector of length equal to the number of coefficients in the final table, after

`coef_omit`

is applied.Named character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c("hp:mpg"="hp X mpg")

Function: Accepts a character vector of the model's term names and returns a named vector like the one described above. The

`modelsummary`

package supplies a`coef_rename()`

function which can do common cleaning tasks:`modelsummary(model, coef_rename = coef_rename)`

- vcov
robust standard errors and other manual statistics. The

`vcov`

argument accepts six types of input (see the 'Details' and 'Examples' sections below):NULL returns the default uncertainty estimates of the model object

string, vector, or (named) list of strings. "iid", "classical", and "constant" are aliases for

`NULL`

, which returns the model's default uncertainty estimates. The strings "HC", "HC0", "HC1" (alias: "stata"), "HC2", "HC3" (alias: "robust"), "HC4", "HC4m", "HC5", "HAC", "NeweyWest", "Andrews", "panel-corrected", "outer-product", and "weave" use variance-covariance matrices computed using functions from the`sandwich`

package, or equivalent method. The behavior of those functions can (and sometimes*must*) be altered by passing arguments to`sandwich`

directly from`modelsummary`

through the ellipsis (`...`

), but it is safer to define your own custom functions as described in the next bullet.function or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g.,

`stats::vcov`

,`sandwich::vcovHC`

,`function(x) vcovPC(x, cluster="country")`

).formula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).

named list of

`length(models)`

variance-covariance matrices with row and column names equal to the names of your coefficient estimates.a named list of length(models) vectors with names equal to the names of your coefficient estimates. See 'Examples' section below. Warning: since this list of vectors can include arbitrary strings or numbers,

`modelsummary`

cannot automatically calculate p values. The`stars`

argument may thus use incorrect significance thresholds when`vcov`

is a list of vectors.

- exponentiate
TRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the

`estimate`

,`conf.low`

, and`conf.high`

statistics are exponentiated, and the`std.error`

is transformed to`exp(estimate)*std.error`

.- 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 side-by-side (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 three functions. See the documentation of these functions for lists of available arguments.

parameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:

`standardize`

,`centrality`

,`dispersion`

,`test`

,`ci_method`

,`prior`

,`diagnostic`

,`rope_range`

,`power`

,`cluster`

, etc.

performance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:

`metrics`

,`estimator`

, etc.

kableExtra::kbl or gt::gt draw tables, depending on the value of the

`output`

argument.

## Examples

```
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' = 'V-shape 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 = "(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)
# logistic regression example
df <- as.data.frame(Titanic)
mod_titanic <- glm(
Survived ~ Class + Sex,
family = binomial,
weight = Freq,
data = df
)
# displaying odds ratio using a log scale
modelplot(mod_titanic, exponentiate = TRUE) +
scale_x_log10() +
xlab("Odds Ratios and 95% confidence intervals")
```

## References

Arel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” *Journal of Statistical Software*, *103*(1), 1-23. doi:10.18637/jss.v103.i01
.'