Model Summary Plots with Estimates and Confidence Intervals

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://modelsummary.com/

modelplot Vignette.

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 .'