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/
msummary(
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,
...
)
a model or (optionally named) list of models
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.
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.
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}]"
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 {}
.
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.
confidence level to use for confidence intervals
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
.
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}
.
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
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.
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.
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.
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'.
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.
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
.
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.
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 = ()]}
list or vector of notes to append to the bottom of the table.
string
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)
}