Create beautiful and customizable tables to summarize several statistical
models side-by-side. This function supports dozens of statistical models,
and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint,
Excel, RTF, JPG, or PNG. The appearance of the tables can be customized
extensively by specifying the `output`

argument, and by using functions from
one of the supported table customization packages: `kableExtra`

, `gt`

,
`flextable`

, `huxtable`

. For more information, see the Details and Examples
sections below, and the vignettes on the `modelsummary`

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

```
modelsummary(
models,
output = "default",
fmt = 3,
estimate = "estimate",
statistic = "std.error",
vcov = NULL,
conf_level = 0.95,
exponentiate = FALSE,
stars = FALSE,
shape = term + statistic ~ model,
coef_map = NULL,
coef_omit = NULL,
coef_rename = NULL,
gof_map = NULL,
gof_omit = NULL,
group_map = NULL,
add_rows = NULL,
align = NULL,
notes = NULL,
title = NULL,
escape = TRUE,
...
)
```

- models
a model or (optionally named) list of models

- output
filename or object type (character string)

Supported filename extensions: .docx, .html, .tex, .md, .txt, .png, .jpg.

Supported object types: "default", "html", "markdown", "latex", "latex_tabular", "data.frame", "gt", "kableExtra", "huxtable", "flextable", "jupyter". The "modelsummary_list" value produces a lightweight object which can be saved and fed back to the

`modelsummary`

function.Warning: Users should not supply a file name to the

`output`

argument if they intend to customize the table with external packages. See the 'Details' section.LaTeX compilation requires the

`booktabs`

and`siunitx`

packages, but`siunitx`

can be disabled or replaced with global options. See the 'Details' section.The default output formats and table-making packages can be modified with global options. See the 'Details' section.

- fmt
determines how to format numeric values

integer: the number of digits to keep after the period

`format(round(x, fmt), nsmall=fmt)`

character: passed to the

`sprintf`

function (e.g., '%.3f' keeps 3 digits with trailing zero). See`?sprintf`

function: returns a formatted character string.

NULL: does not format numbers, which allows users to include function in the "glue" strings in the

`estimate`

and`statistic`

arguments.LaTeX output: To ensure proper typography, all numeric entries are enclosed in the

`\num{}`

command, which requires the`siunitx`

package to be loaded in the LaTeX preamble. This behavior can be altered with global options. See the 'Details' section.

- estimate
a single string or a character vector of length equal to the number of models. Valid entries include any column name of the data.frame produced by

`get_estimates(model)`

, and strings with curly braces compatible with the`glue`

package format. Examples:`"estimate"`

`"{estimate} ({std.error}){stars}"`

`"{estimate} [{conf.low}, {conf.high}]"`

- statistic
vector of strings or

`glue`

strings which select uncertainty statistics to report vertically below the estimate. NULL omits all uncertainty statistics."conf.int", "std.error", "statistic", "p.value", "conf.low", "conf.high", or any column name produced by:

`get_estimates(model)`

`glue`

package strings with braces, with or without R functions, such as:`"{p.value} [{conf.low}, {conf.high}]"`

`"Std.Error: {std.error}"`

`"exp(estimate) * std.error"

Numbers are automatically rounded and converted to strings. To apply functions to their numeric values, as in the last

`glue`

example, users must set`fmt=NULL`

.Parentheses are added automatically unless the string includes

`glue`

curly braces`{}`

.

- 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.

- conf_level
confidence level to use for confidence intervals

- 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`

.- stars
to indicate statistical significance

FALSE (default): no significance stars.

TRUE: +=.1, *=.05, **=.01, ***=0.001

Named numeric vector for custom stars such as

`c('*' = .1, '+' = .05)`

Note: a legend will not be inserted at the bottom of the table when the

`estimate`

or`statistic`

arguments use "glue strings" with`{stars}`

.

- shape
formula which determines the shape of the table. The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include a group identifier to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). This identifier must be one of the column names produced by:

`get_estimates(model)`

. If an incomplete formula is supplied (e.g.,`~statistic`

),`modelsummary`

tries to complete it automatically. Potential`shape`

values include:`term + statistic ~ model`

: default`term ~ model + statistic`

: statistics in separate columns`model + statistic ~ term`

: models in rows and terms in columns`term + response + statistic ~ model`

:`term ~ response`

- 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
string regular expression (perl-compatible) used to determine which coefficients to omit from the table. A "negative lookahead" can be used to specify which coefficients to

*keep*in the table. Examples:`"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
named character vector or function which returns a named vector. Values of the vector refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object, e.g. c("hp:mpg"="hp X mpg") for an interaction term. If you provide a function to

`coef_rename`

,`modelsummary`

will create a named vector for you by deriving the new variable names from the vector of original term names with your function.- gof_map
rename, reorder, and omit goodness-of-fit statistics and other model information. This argument accepts 4 types of values:

NULL (default): the

`modelsummary::gof_map`

dictionary is used for formatting, and all unknown statistic are included.NA: excludes all statistics from the bottom part of the table.

character vector such as

`c("rmse", "nobs", "r.squared")`

. Elements correspond to colnames in the data.frame produced by`get_gof(model)`

. The default dictionary is used to format and rename statistics.data.frame with 3 columns named "raw", "clean", "fmt". Unknown statistics are omitted. See the 'Examples' section below.

list of lists, each of which includes 3 elements named "raw", "clean", "fmt". Unknown statistics are omitted. See the 'Examples section below'.

- gof_omit
string regular expression (perl-compatible) used to determine which statistics to omit from the bottom section of the table. A "negative lookahead" can be used to specify which statistics to

*keep*in the table. Examples:`"IC"`

: omit statistics matching the "IC" substring.`"BIC|AIC"`

: omit statistics matching the "AIC" or "BIC" substrings.`"^(?!.*IC)"`

: keep statistics matching the "IC" substring.

- group_map
named or unnamed character vector. Subset, rename, and reorder coefficient groups specified a grouping variable specified in the

`shape`

argument formula. This argument behaves like`coef_map`

.- 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.

- align
A string with a number of characters equal to the number of columns in the table (e.g.,

`align = "lcc"`

). Valid characters: l, c, r, d."l": left-aligned column

"c": centered column

"r": right-aligned column

"d": dot-aligned column. Only supported for LaTeX/PDF tables produced by

`kableExtra`

. These commands must appear in the LaTeX preamble (they are added automatically when compiling Rmarkdown documents to PDF):`\usepackage{booktabs}`

`\usepackage{siunitx}`

`\newcolumntype{d}{S[input-symbols = ()]}`

- notes
list or vector of notes to append to the bottom of the table.

- title
string

- escape
boolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. This setting does not affect captions or notes.

- ...
all other arguments are passed through to the extractor and table-making functions (by default

`broom::tidy`

and`kableExtra::kbl`

, but this can be customized). This allows users to pass arguments directly to`modelsummary`

in order to affect the behavior of other functions behind the scenes. For example,`metrics="none"`

,`metrics="all"`

, or`metrics=c("R2", "RMSE")`

to select the goodness-of-fit extracted by the`performance`

package (must have set`options(modelsummary_get="easystats")`

first). This can be useful for some models when statistics take a long time to compute. See`?performance::performance`

a regression table in a format determined by the `output`

argument.

`output`

The `modelsummary_list`

output is a lightweight format which can be used to save model results, so they can be fed back to `modelsummary`

later to avoid extracting results again.

When a file name with a valid extension is supplied to the `output`

argument,
the table is written immediately to file. If you want to customize your table
by post-processing it with an external package, you need to choose a
different output format and saving mechanism. Unfortunately, the approach
differs from package to package:

`gt`

: set`output="gt"`

, post-process your table, and use the`gt::gtsave`

function.`kableExtra`

: set`output`

to your destination format (e.g., "latex", "html", "markdown"), post-process your table, and use`kableExtra::save_kable`

function.

`vcov`

To use a string such as "robust" or "HC0", your model must be supported
by the `sandwich`

package. This includes objects such as: lm, glm,
survreg, coxph, mlogit, polr, hurdle, zeroinfl, and more.

NULL, "classical", "iid", and "constant" are aliases which do not modify uncertainty estimates and simply report the default standard errors stored in the model object.

One-sided formulas such as `~clusterid`

are passed to the `sandwich::vcovCL`

function.

Matrices and functions producing variance-covariance matrices are first
passed to `lmtest`

. If this does not work, `modelsummary`

attempts to take
the square root of the diagonal to adjust "std.error", but the other
uncertainty estimates are not be adjusted.

Numeric vectors are formatted according to `fmt`

and placed in brackets.
Character vectors printed as given, without parentheses.

If your model type is supported by the `lmtest`

package, the
`vcov`

argument will try to use that package to adjust all the
uncertainty estimates, including "std.error", "statistic", "p.value", and
"conf.int". If your model is not supported by `lmtest`

, only the "std.error"
will be adjusted by, for example, taking the square root of the matrix's
diagonal.

The behavior of `modelsummary`

can be affected by setting global options:

`modelsummary_factory_default`

`modelsummary_factory_latex`

`modelsummary_factory_html`

`modelsummary_factory_png`

`modelsummary_get`

`modelsummary_format_numeric_latex`

`modelsummary_format_numeric_html`

`modelsummary`

supports 4 table-making packages: `kableExtra`

, `gt`

,
`flextable`

, and `huxtable`

. Some of these packages have overlapping
functionalities. For example, 3 of those packages can export to LaTeX. To
change the default backend used for a specific file format, you can use
the `options`

function:

`options(modelsummary_factory_html = 'kableExtra')`

`options(modelsummary_factory_latex = 'gt')`

`options(modelsummary_factory_word = 'huxtable')`

`options(modelsummary_factory_png = 'gt')`

`modelsummary`

can use two sets of packages to extract information from
statistical models: `broom`

and the `easystats`

family (`performance`

and
`parameters`

). By default, it uses `broom`

first and `easystats`

as a
fallback if `broom`

fails. You can change the order of priorities
or include goodness-of-fit extracted by *both* packages by setting:

`options(modelsummary_get = "broom")`

`options(modelsummary_get = "easystats")`

`options(modelsummary_get = "all")`

By default, LaTeX tables enclose all numeric entries in the `\num{}`

command
from the siunitx package. To prevent this behavior, or to enclose numbers
in dollar signs (for LaTeX math mode), users can call:

`options(modelsummary_format_numeric_latex = "plain")`

`options(modelsummary_format_numeric_latex = "mathmode")`

A similar option can be used to display numerical entries using MathJax in HTML tables:

`options(modelsummary_format_numeric_html = "mathjax")`

It can take a long time to compute and extract summary statistics from certain models (e.g., Bayesian). In those cases, users can parallelize the process. Since parallelization occurs at the model level, no speedup is available for tables with a single model. To use parallel computation, all users have to do is load the `future.apply`

package and call the `plan()`

function. Example:

```
library(future.apply)
plan("multisession")
modelsummary(model_list)
```

```
if (FALSE) {
# The `modelsummary` website includes \emph{many} examples and tutorials:
# https://vincentarelbundock.github.io/modelsummary
library(modelsummary)
# load data and estimate models
data(trees)
models <- list()
models[['Bivariate']] <- lm(Girth ~ Height, data = trees)
models[['Multivariate']] <- lm(Girth ~ Height + Volume, data = trees)
# simple table
modelsummary(models)
# statistic
modelsummary(models, statistic = NULL)
modelsummary(models, statistic = 'p.value')
modelsummary(models, statistic = 'statistic')
modelsummary(models, statistic = 'conf.int', conf_level = 0.99)
modelsummary(models, statistic = c("t = {statistic}",
"se = {std.error}",
"conf.int"))
# estimate
modelsummary(models,
statistic = NULL,
estimate = "{estimate} [{conf.low}, {conf.high}]")
modelsummary(models,
estimate = c("{estimate}{stars}",
"{estimate} ({std.error})"))
# vcov
modelsummary(models, vcov = "robust")
modelsummary(models, vcov = list("classical", "stata"))
modelsummary(models, vcov = sandwich::vcovHC)
modelsummary(models,
vcov = list(stats::vcov, sandwich::vcovHC))
modelsummary(models,
vcov = list(c("(Intercept)"="", "Height"="!"),
c("(Intercept)"="", "Height"="!", "Volume"="!!")))
# vcov with custom names
modelsummary(
models,
vcov = list("Stata Corp" = "stata",
"Newey Lewis & the News" = "NeweyWest"))
# coef_rename
modelsummary(models, coef_rename = c('Volume' = 'Large', 'Height' = 'Tall'))
modelsummary(models, coef_rename = toupper)
modelsummary(models, coef_rename = coef_rename)
# coef_map
modelsummary(models, coef_map = c('Volume' = 'Large', 'Height' = 'Tall'))
modelsummary(models, coef_map = c('Volume', 'Height'))
# coef_omit: omit coefficients matching one substring
modelsummary(models, coef_omit = "ei", omit = ".*")
# coef_omit: omit a specific coefficient
modelsummary(models, coef_omit = "^Volume$", gof_omit = ".*", output = "markdown")
# coef_omit: omit coefficients matching either one of two substring
modelsummary(models, coef_omit = "ei|rc", omit = ".*")
# coef_omit: keep coefficients starting with a substring (using a negative lookahead)
modelsummary(models, coef_omit = "^(?!Vol)", omit = ".*")
# coef_omit: keep coefficients matching a substring
modelsummary(models, coef_omit = "^(?!.*ei|.*pt)", omit = ".*")
# shape
library(nnet)
multi <- multinom(factor(cyl) ~ mpg + hp, data = mtcars, trace = FALSE)
modelsummary(multi, shape = y.level ~ model)
modelsummary(multi, shape = term ~ y.level)
# title
modelsummary(models, title = 'This is the title')
# title with LaTeX label (for numbering and referencing)
modelsummary(models, title = 'This is the title \\label{tab:description}')
# add_rows
rows <- tibble::tribble(~term, ~Bivariate, ~Multivariate,
'Empty row', '-', '-',
'Another empty row', '?', '?')
attr(rows, 'position') <- c(1, 3)
modelsummary(models, add_rows = rows)
# notes
modelsummary(models, notes = list('A first note', 'A second note'))
# gof_map: tribble
library(tibble)
gm <- tribble(
~raw, ~clean, ~fmt,
"r.squared", "R Squared", 5)
modelsummary(models, gof_map = gm)
# gof_map: data.frame
gm <- modelsummary::gof_map
gof_custom$omit[gof_custom$raw == 'deviance'] <- FALSE
gof_custom$fmt[gof_custom$raw == 'r.squared'] <- "%.5f"
modelsummary(models, gof_map = gof_custom)
# gof_map: list of lists
f1 <- function(x) format(round(x, 3), big.mark=",")
f2 <- function(x) format(round(x, 0), big.mark=",")
gm <- list(
list("raw" = "nobs", "clean" = "N", "fmt" = f2),
list("raw" = "AIC", "clean" = "aic", "fmt" = f1))
modelsummary(models,
fmt = f1,
gof_map = gm)
}
```