modelsummary: regression tables

modelsummary includes a powerful set of utilities to customize the information displayed in your model summary tables. You can easily rename, reorder, subset or omit parameter estimates; choose the set of goodness-of-fit statistics to display; display various “robust” standard errors or confidence intervals; add titles, footnotes, or source notes; insert stars or custom characters to indicate levels of statistical significance; or add rows with supplemental information about your models.

library(modelsummary)
library(kableExtra)
library(gt)

url <- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
dat <- read.csv(url)

models <- list(
  "OLS 1"     = lm(Donations ~ Literacy + Clergy, data = dat),
  "Poisson 1" = glm(Donations ~ Literacy + Commerce, family = poisson, data = dat),
  "OLS 2"     = lm(Crime_pers ~ Literacy + Clergy, data = dat),
  "Poisson 2" = glm(Crime_pers ~ Literacy + Commerce, family = poisson, data = dat),
  "OLS 3"     = lm(Crime_prop ~ Literacy + Clergy, data = dat)
)

modelsummary(models)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.667	8.241	16259.384	9.876	11243.544
	(2078.276)	(0.006)	(2611.140)	(0.003)	(1011.240)
Literacy	-39.121	0.003	3.680	0.000	-68.507
	(37.052)	(0.000)	(46.552)	(0.000)	(18.029)
Clergy	15.257		77.148		-16.376
	(25.735)		(32.334)		(12.522)
Commerce		0.011		0.001
		(0.000)		(0.000)
Num.Obs.	86	86	86	86	86
R2	0.020		0.065		0.152
R2 Adj.	-0.003		0.043		0.132
AIC	1740.8	274160.8	1780.0	257564.4	1616.9
BIC	1750.6	274168.2	1789.9	257571.7	1626.7
Log.Lik.	-866.392	-137077.401	-886.021	-128779.186	-804.441
F	0.866	18294.559	2.903	279.956	7.441
RMSE	5740.99	5491.61	7212.97	7451.70	2793.43

`output`: print and save

The output argument determines the type of object returned by modelsummary and/or the file where this table should be written.

If you want to save a table directly to file, you can type:

modelsummary(models, output = "table.docx")
modelsummary(models, output = "table.html")
modelsummary(models, output = "table.tex")
modelsummary(models, output = "table.md")
modelsummary(models, output = "table.txt")
modelsummary(models, output = "table.png")

If you want a raw HTML, LaTeX, or Markdown table, you can type:

modelsummary(models, output = "html")
modelsummary(models, output = "latex")
modelsummary(models, output = "markdown")

If you to customize the appearance of your table using external tools like gt, kableExtra, flextable, or huxtable, you can type:

modelsummary(models, output = "gt")
modelsummary(models, output = "kableExtra")
modelsummary(models, output = "flextable")
modelsummary(models, output = "huxtable")

Warning: When a file name 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.

`fmt`: round and format

The fmt argument defines how numeric values are rounded and presented in the table. This argument accepts three types of input:

Integer: Number of decimal digits
User-supplied function: Accepts a numeric vector and returns a character vector of the same length.
modelsummary function: fmt_decimal(), fmt_significant(), fmt_sprintf(), fmt_term(), fmt_statistic, fmt_identity()

Examples:

mod <- lm(mpg ~ hp + drat + qsec, data = mtcars)

# decimal digits
modelsummary(mod, fmt = 3)

# user-supplied function
modelsummary(mod, fmt = function(x) round(x, 2))

# p values with different number of digits
modelsummary(mod, fmt = fmt_decimal(1, 3), statistic = c("std.error", "p.value"))

# significant digits
modelsummary(mod, fmt = fmt_significant(3))

# sprintf(): decimal digits
modelsummary(mod, fmt = fmt_sprintf("%.5f"))

# sprintf(): scientific notation 
modelsummary(mod, fmt = fmt_sprintf("%.5e"))

# statistic-specific formatting
modelsummary(mod, fmt = fmt_statistic(estimate = 4, conf.int = 1), statistic = "conf.int")

# term-specific formatting
modelsummary(mod, fmt = fmt_term(hp = 4, drat = 1, default = fmt_significant(2)))

modelsummary(mod, fmt = NULL)

Custom formatting function with big mark commas:

modf <- lm(I(mpg * 100) ~ hp, mtcars)
f <- function(x) formatC(x, digits = 2, big.mark = ",", format = "f")
modelsummary(modf, fmt = f, gof_map = NA)

	(1)
(Intercept)	3,009.89
	(163.39)
hp	-6.82
	(1.01)

In many languages the comma is used as a decimal mark instead of the period. modelsummary respects the global R OutDec option, so you can simply execute this command and your tables will be adjusted automatically:

options(OutDec=",")

`estimate`

By default, modelsummary prints each coefficient estimate on its own row. You can customize this by changing the estimate argument. For example, this would produce a table of p values instead of coefficient estimates:

modelsummary(models, estimate = "p.value")

You can also use glue string, using curly braces to specify the statistics you want. For example, this displays the estimate next to a confidence interval:

modelsummary(
  models,
  fmt = 1,
  estimate  = "{estimate} [{conf.low}, {conf.high}]",
  statistic = NULL,
  coef_omit = "Intercept")

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
Literacy	-39.1 [-112.8, 34.6]	0.0 [0.0, 0.0]	3.7 [-88.9, 96.3]	0.0 [0.0, 0.0]	-68.5 [-104.4, -32.6]
Clergy	15.3 [-35.9, 66.4]		77.1 [12.8, 141.5]		-16.4 [-41.3, 8.5]
Commerce		0.0 [0.0, 0.0]		0.0 [0.0, 0.0]
Num.Obs.	86	86	86	86	86
R2	0.020		0.065		0.152
R2 Adj.	-0.003		0.043		0.132
AIC	1740.8	274160.8	1780.0	257564.4	1616.9
BIC	1750.6	274168.2	1789.9	257571.7	1626.7
Log.Lik.	-866.392	-137077.401	-886.021	-128779.186	-804.441
F	0.866	18294.559	2.903	279.956	7.441
RMSE	5740.99	5491.61	7212.97	7451.70	2793.43

Glue strings can also apply R functions to estimates. However, since modelsummary rounds numbers and transforms them to character by default, we must set fmt = NULL:

m <- glm(am ~ mpg, data = mtcars, family = binomial)
modelsummary(
    m,
    fmt = NULL,
    estimate = "{round(exp(estimate), 5)}",
    statistic = "{round(exp(estimate) * std.error, 3)}")

	(1)
(Intercept)	0.00136
	0.003
mpg	1.35938
	0.156
Num.Obs.	32
AIC	33.7
BIC	36.6
Log.Lik.	-14.838
F	7.148
RMSE	0.39

You can also use different estimates for different models by using a vector of strings:

modelsummary(
  models,
  fmt = 1,
  estimate  = c("estimate",
                "{estimate}{stars}",
                "{estimate} ({std.error})",
                "{estimate} ({std.error}){stars}",
                "{estimate} [{conf.low}, {conf.high}]"),
  statistic = NULL,
  coef_omit = "Intercept")

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
Literacy	-39.1	0.0***	3.7 (46.6)	0.0 (0.0)***	-68.5 [-104.4, -32.6]
Clergy	15.3		77.1 (32.3)		-16.4 [-41.3, 8.5]
Commerce		0.0***		0.0 (0.0)***
Num.Obs.	86	86	86	86	86
R2	0.020		0.065		0.152
R2 Adj.	-0.003		0.043		0.132
AIC	1740.8	274160.8	1780.0	257564.4	1616.9
BIC	1750.6	274168.2	1789.9	257571.7	1626.7
Log.Lik.	-866.392	-137077.401	-886.021	-128779.186	-804.441
F	0.866	18294.559	2.903	279.956	7.441
RMSE	5740.99	5491.61	7212.97	7451.70	2793.43

`statistic`: SE, t, p, CI, etc.

By default, modelsummary prints the coefficient’s standard error in parentheses below the corresponding estimate. The value of this uncertainty statistic is determined by the statistic argument. The statistic argument accepts any of the column names produced by get_estimates(model). For example:

modelsummary(models, statistic = 'std.error')
modelsummary(models, statistic = 'p.value')
modelsummary(models, statistic = 'statistic')

You can also display confidence intervals in brackets by setting statistic="conf.int":

modelsummary(models,
             fmt = 1,
             statistic = 'conf.int', 
             conf_level = .99)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.7	8.2	16259.4	9.9	11243.5
	[2469.6, 13427.8]	[8.2, 8.3]	[9375.5, 23143.3]	[9.9, 9.9]	[8577.5, 13909.5]
Literacy	-39.1	0.0	3.7	0.0	-68.5
	[-136.8, 58.6]	[0.0, 0.0]	[-119.0, 126.4]	[0.0, 0.0]	[-116.0, -21.0]
Clergy	15.3		77.1		-16.4
	[-52.6, 83.1]		[-8.1, 162.4]		[-49.4, 16.6]
Commerce		0.0		0.0
		[0.0, 0.0]		[0.0, 0.0]
Num.Obs.	86	86	86	86	86
R2	0.020		0.065		0.152
R2 Adj.	-0.003		0.043		0.132
AIC	1740.8	274160.8	1780.0	257564.4	1616.9
BIC	1750.6	274168.2	1789.9	257571.7	1626.7
Log.Lik.	-866.392	-137077.401	-886.021	-128779.186	-804.441
F	0.866	18294.559	2.903	279.956	7.441
RMSE	5740.99	5491.61	7212.97	7451.70	2793.43

Alternatively, you can supply a glue string to get more complicated results:

modelsummary(models,
             statistic = "{std.error} ({p.value})")

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.667	8.241	16259.384	9.876	11243.544
	2078.276 (<0.001)	0.006 (<0.001)	2611.140 (<0.001)	0.003 (<0.001)	1011.240 (<0.001)
Literacy	-39.121	0.003	3.680	0.000	-68.507
	37.052 (0.294)	0.000 (<0.001)	46.552 (0.937)	0.000 (<0.001)	18.029 (<0.001)
Clergy	15.257		77.148		-16.376
	25.735 (0.555)		32.334 (0.019)		12.522 (0.195)
Commerce		0.011		0.001
		0.000 (<0.001)		0.000 (<0.001)
Num.Obs.	86	86	86	86	86
R2	0.020		0.065		0.152
R2 Adj.	-0.003		0.043		0.132
AIC	1740.8	274160.8	1780.0	257564.4	1616.9
BIC	1750.6	274168.2	1789.9	257571.7	1626.7
Log.Lik.	-866.392	-137077.401	-886.021	-128779.186	-804.441
F	0.866	18294.559	2.903	279.956	7.441
RMSE	5740.99	5491.61	7212.97	7451.70	2793.43

You can also display several different uncertainty estimates below the coefficient estimates by using a vector. For example,

modelsummary(models, gof_omit = ".*",
             statistic = c("conf.int",
                           "s.e. = {std.error}", 
                           "t = {statistic}",
                           "p = {p.value}"))

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.667	8.241	16259.384	9.876	11243.544
	[3815.060, 12082.275]	[8.230, 8.252]	[11065.933, 21452.836]	[9.869, 9.883]	[9232.228, 13254.860]
	s.e. = 2078.276	s.e. = 0.006	s.e. = 2611.140	s.e. = 0.003	s.e. = 1011.240
	t = 3.825	t = 1408.907	t = 6.227	t = 2864.987	t = 11.119
	p = <0.001	p = <0.001	p = <0.001	p = <0.001	p = <0.001
Literacy	-39.121	0.003	3.680	0.000	-68.507
	[-112.816, 34.574]	[0.003, 0.003]	[-88.910, 96.270]	[0.000, 0.000]	[-104.365, -32.648]
	s.e. = 37.052	s.e. = 0.000	s.e. = 46.552	s.e. = 0.000	s.e. = 18.029
	t = -1.056	t = 33.996	t = 0.079	t = -4.989	t = -3.800
	p = 0.294	p = <0.001	p = 0.937	p = <0.001	p = <0.001
Clergy	15.257		77.148		-16.376
	[-35.930, 66.443]		[12.837, 141.459]		[-41.282, 8.530]
	s.e. = 25.735		s.e. = 32.334		s.e. = 12.522
	t = 0.593		t = 2.386		t = -1.308
	p = 0.555		p = 0.019		p = 0.195
Commerce		0.011		0.001
		[0.011, 0.011]		[0.001, 0.001]
		s.e. = 0.000		s.e. = 0.000
		t = 174.542		t = 15.927
		p = <0.001		p = <0.001

Setting statistic=NULL omits all statistics. This can often be useful if, for example, you want to display confidence intervals next to coefficients:

modelsummary(models, gof_omit = ".*",
             estimate = "{estimate} [{conf.low}, {conf.high}]",
             statistic = NULL)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.667 [3815.060, 12082.275]	8.241 [8.230, 8.252]	16259.384 [11065.933, 21452.836]	9.876 [9.869, 9.883]	11243.544 [9232.228, 13254.860]
Literacy	-39.121 [-112.816, 34.574]	0.003 [0.003, 0.003]	3.680 [-88.910, 96.270]	0.000 [0.000, 0.000]	-68.507 [-104.365, -32.648]
Clergy	15.257 [-35.930, 66.443]		77.148 [12.837, 141.459]		-16.376 [-41.282, 8.530]
Commerce		0.011 [0.011, 0.011]		0.001 [0.001, 0.001]

`vcov`: robust SEs

You can use clustered or robust uncertainty estimates by modifying the vcov parameter. This function accepts 5 different types of input. You can use a string or a vector of strings:

modelsummary(models, vcov = "robust")
modelsummary(models, vcov = c("classical", "robust", "bootstrap", "stata", "HC4"))

These variance-covariance matrices are calculated using the sandwich package. You can pass arguments to the sandwich functions directly from the modelsummary function. For instance, to change the number of bootstrap replicates and to specify a clustering variable we could call:

modelsummary(mod, vcov = "bootstrap", R = 1000, cluster = "country")

You can use a one-sided formula or list of one-sided formulas to use clustered standard errors:

modelsummary(models, vcov = ~Region)

You can specify a function that produces variance-covariance matrices:

library(sandwich)
modelsummary(models, vcov = vcovHC)

You can supply a list of functions of the same length as your model list:

modelsummary(models, 
  vcov = list(vcov, vcovHC, vcovHAC, vcovHC, vcov))

You can supply a list of named variance-covariance matrices:

vcov_matrices <- lapply(models, vcovHC)
modelsummary(models, vcov = vcov_matrices)

You can supply a list of named vectors:

vc <- list(
  `OLS 1` = c(`(Intercept)` = 2, Literacy = 3, Clergy = 4), 
  `Poisson 1` = c(`(Intercept)` = 3, Literacy = -5, Commerce = 3),
  `OLS 2` = c(`(Intercept)` = 7, Literacy = -6, Clergy = 9), 
  `Poisson 2` = c(`(Intercept)` = 4, Literacy = -7, Commerce = -9),
  `OLS 3` = c(`(Intercept)` = 1, Literacy = -5, Clergy = -2))
modelsummary(models, vcov = vc)

`stars`

Some people like to add “stars” to their model summary tables to mark statistical significance. The stars argument can take three types of input:

NULL omits any stars or special marks (default)
TRUE uses these default values: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Named numeric vector for custom stars.

modelsummary(models)
modelsummary(models, stars = TRUE) 
modelsummary(models, stars = c('+' = .1, '&' = .01))

Whenever stars is not NULL, modelsummary adds a note at the bottom of the table automatically. If you would like to add stars but not include a note at the bottom of the table, you can define the display of your estimate manually using a glue string, as described in the estimate argument section of the documentation. Whenever the stars string appears in the estimate or statistic arguments, modelsummary will assume that you want fine-grained control over your table, and will not include a note about stars.

modelsummary(models,
             estimate = "{estimate}{stars}",
             gof_omit = ".*")

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.667***	8.241***	16259.384***	9.876***	11243.544***
	(2078.276)	(0.006)	(2611.140)	(0.003)	(1011.240)
Literacy	-39.121	0.003***	3.680	0.000***	-68.507***
	(37.052)	(0.000)	(46.552)	(0.000)	(18.029)
Clergy	15.257		77.148*		-16.376
	(25.735)		(32.334)		(12.522)
Commerce		0.011***		0.001***
		(0.000)		(0.000)

If you want to create your own stars description, you can add custom notes with the notes argument.

`coef_omit`

An alternative mechanism to subset coefficients is to use the coef_omit argument, which accepts a vector of integer or a regular expression. For example, we can omit the first and second coefficients as follows:

modelsummary(models, coef_omit = 1:2, gof_map = NA)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
Clergy	15.257		77.148		-16.376
	(25.735)		(32.334)		(12.522)
Commerce		0.011		0.001
		(0.000)		(0.000)

Negative indices determine which coefficients to keep:

modelsummary(models, coef_omit = c(-1, -2), gof_map = NA)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.667	8.241	16259.384	9.876	11243.544
	(2078.276)	(0.006)	(2611.140)	(0.003)	(1011.240)
Literacy	-39.121	0.003	3.680	0.000	-68.507
	(37.052)	(0.000)	(46.552)	(0.000)	(18.029)

When coef_omit is a string, it is fed to grepl(x,perl=TRUE) to detect the variable names which should be excluded from the table.

modelsummary(models, coef_omit = "Intercept|.*merce", gof_map = NA)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
Literacy	-39.121	0.003	3.680	0.000	-68.507
	(37.052)	(0.000)	(46.552)	(0.000)	(18.029)
Clergy	15.257		77.148		-16.376
	(25.735)		(32.334)		(12.522)

Since coef_omit accepts regexes, you can do interesting things with it, such as specifying the list of variables that modelsummary should keep instead of omit. To do this, we use a negative lookahead. To keep only the coefficients starting with “Lit”, we call:

modelsummary(models, coef_omit = "^(?!Lit)", gof_map = NA)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
Literacy	-39.121	0.003	3.680	0.000	-68.507
	(37.052)	(0.000)	(46.552)	(0.000)	(18.029)

To keep all coefficients matching the “y” substring:

modelsummary(models, coef_omit = "^(?!.*y)", gof_map = NA)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
Literacy	-39.121	0.003	3.680	0.000	-68.507
	(37.052)	(0.000)	(46.552)	(0.000)	(18.029)
Clergy	15.257		77.148		-16.376
	(25.735)		(32.334)		(12.522)

To keep all coefficients matching one of two substrings:

modelsummary(models, coef_omit = "^(?!.*tercept|.*y)", gof_map = NA)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.667	8.241	16259.384	9.876	11243.544
	(2078.276)	(0.006)	(2611.140)	(0.003)	(1011.240)
Literacy	-39.121	0.003	3.680	0.000	-68.507
	(37.052)	(0.000)	(46.552)	(0.000)	(18.029)
Clergy	15.257		77.148		-16.376
	(25.735)		(32.334)		(12.522)

`coef_rename`

modelsummary offers powerful and innovative mechanisms to rename, reorder, and subset coefficients and goodness-of-fit statistics.

You can rename coefficients using the coef_rename argument. For example, if you have two models with different explanatory variables, but you want both variables to have the same name and appear on the same row, you can do:

x <- list(lm(hp ~ drat, mtcars),
          lm(hp ~ vs, mtcars))

modelsummary(x, coef_rename = c("drat" = "Explanator", "vs" = "Explanator"))

	(1)	(2)
(Intercept)	353.653	189.722
	(76.049)	(11.347)
Explanator	-57.545	-98.365
	(20.922)	(17.155)
Num.Obs.	32	32
R2	0.201	0.523
R2 Adj.	0.175	0.507
AIC	359.2	342.7
BIC	363.6	347.1
Log.Lik.	-176.588	-168.347
F	7.565	32.876
RMSE	60.31	46.61

If you provide a named character vector to coef_rename, only exact matches of the complete original term name will be replaced.

For complex modifications, you can feed a function which returns a named vector to the coef_rename argument. For example, modelsummary ships with a function called coef_rename, which executes some common renaming tasks automatically. This example also uses the dvnames function to extract the name of the dependent variable in each model:

x <- list(
  lm(mpg ~ factor(cyl) + drat + disp, data = mtcars),
  lm(hp ~ factor(cyl) + drat + disp, data = mtcars)
)

modelsummary(dvnames(x), coef_rename = coef_rename)

	mpg	hp
(Intercept)	26.158	-86.788
	(6.537)	(79.395)
6	-4.547	46.485
	(1.731)	(21.027)
8	-4.580	121.892
	(2.952)	(35.853)
Drat	0.783	37.815
	(1.478)	(17.952)
Disp	-0.026	0.147
	(0.011)	(0.137)
Num.Obs.	32	32
R2	0.786	0.756
R2 Adj.	0.754	0.720
AIC	167.4	327.2
BIC	176.2	336.0
Log.Lik.	-77.719	-157.623
F	24.774	20.903
RMSE	2.74	33.34

Of course, you can also define your own custom functions. For instance, to rename a model with interacted variables (e.g., “drat:mpg”), you could define a custom rename_explanator function:

y <- list(
  lm(hp ~ drat / mpg, mtcars),
  lm(hp ~ vs / mpg, mtcars)
)

rename_explanator <- function(old_names) {
  new_names <- gsub("drat|vs", "Explanator", old_names)
  setNames(new_names, old_names)
}

modelsummary(y, coef_rename = rename_explanator)

	(1)	(2)
(Intercept)	91.206	189.722
	(72.344)	(11.205)
Explanator	68.331	-18.316
	(27.390)	(62.531)
Explanator:mpg	-2.558	-3.260
	(0.467)	(2.451)
Num.Obs.	32	32
R2	0.608	0.550
R2 Adj.	0.581	0.519
AIC	338.4	342.8
BIC	344.3	348.7
Log.Lik.	-165.218	-167.399
F	22.454	17.743
RMSE	42.27	45.25

Beware of inadvertently replacing parts of other variable names! Making your regex pattern as specific as possible (e.g., by adding word boundaries) is likely a good idea. The custom rename function is also a good place to re-introduce the replacement of “:” with “×” if you are dealing with interaction terms – modelsummary makes this replacement for you only when the coef_rename argument is not specified.

Another possibility is to assign variable labels to attributes in the data used to fit the model. Then, we can automatically rename them:

datlab <- mtcars
datlab$cyl <- factor(datlab$cyl)
attr(datlab$cyl, "label") <- "Cylinders"
attr(datlab$am, "label") <- "Transmission"
modlab <- lm(mpg ~ cyl + am, data = datlab)
modelsummary(modlab, coef_rename = TRUE)

	(1)
(Intercept)	24.802
	(1.323)
Cylinders [6]	-6.156
	(1.536)
Cylinders [8]	-10.068
	(1.452)
Transmission	2.560
	(1.298)
Num.Obs.	32
R2	0.765
R2 Adj.	0.740
AIC	168.4
BIC	175.7
Log.Lik.	-79.199
F	30.402
RMSE	2.87

`coef_map`: order, omit, rename

The coef_map argument is a named vector which allows users to rename, reorder, and subset coefficient estimates. Values of this vector correspond to the “clean” variable name. Names of this vector correspond to the “raw” variable name. The table will be sorted in the order in which terms are presented in coef_map. Coefficients which are not included in coef_map will be excluded from the table.

cm <- c('Literacy'    = 'Literacy (%)',
        'Commerce'    = 'Patents per capita',
        '(Intercept)' = 'Constant')
modelsummary(models, coef_map = cm)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
Literacy (%)	-39.121	0.003	3.680	0.000	-68.507
	(37.052)	(0.000)	(46.552)	(0.000)	(18.029)
Patents per capita		0.011		0.001
		(0.000)		(0.000)
Constant	7948.667	8.241	16259.384	9.876	11243.544
	(2078.276)	(0.006)	(2611.140)	(0.003)	(1011.240)
Num.Obs.	86	86	86	86	86
R2	0.020		0.065		0.152
R2 Adj.	-0.003		0.043		0.132
AIC	1740.8	274160.8	1780.0	257564.4	1616.9
BIC	1750.6	274168.2	1789.9	257571.7	1626.7
Log.Lik.	-866.392	-137077.401	-886.021	-128779.186	-804.441
F	0.866	18294.559	2.903	279.956	7.441
RMSE	5740.99	5491.61	7212.97	7451.70	2793.43

`gof_omit`: goodness-of-fit and model characteristics

gof_omit is a regular expression which will be fed to grepl(x,perl=TRUE) to detect the names of the statistics which should be excluded from the table.

modelsummary(models, gof_omit = 'DF|Deviance|R2|AIC|BIC')

gof_map

The gof_map argument can be used to rename, re-order, subset, and format the statistics displayed in the bottom section of the table (“goodness-of-fit”).

The first type of values allowed is a character vector with elements equal to column names in the data.frame produced by get_gof(model):

modelsummary(models, gof_map = c("nobs", "r.squared"))

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.667	8.241	16259.384	9.876	11243.544
	(2078.276)	(0.006)	(2611.140)	(0.003)	(1011.240)
Literacy	-39.121	0.003	3.680	0.000	-68.507
	(37.052)	(0.000)	(46.552)	(0.000)	(18.029)
Clergy	15.257		77.148		-16.376
	(25.735)		(32.334)		(12.522)
Commerce		0.011		0.001
		(0.000)		(0.000)
Num.Obs.	86	86	86	86	86
R2	0.020		0.065		0.152

A more powerful mechanism is to supply a data.frame (or tibble) through the gof_map argument. This data.frame must include 3 columns:

raw: a string with the name of a column produced by get_gof(model).
clean: a string with the “clean” name of the statistic you want to appear in your final table.
fmt: a string which will be used to round/format the string in question (e.g., "%.3f"). This follows the same standards as the fmt argument in ?modelsummary.

You can see an example of a valid data frame by typing modelsummary::gof_map. This is the default data.frame that modelsummary uses to subset and reorder goodness-of-fit statistics. As you can see, omit == TRUE for quite a number of statistics. You can include setting omit == FALSE:

gm <- modelsummary::gof_map
gm$omit <- FALSE
modelsummary(models, gof_map = gm)

The goodness-of-fit statistics will be printed in the table in the same order as in the gof_map data.frame.

f <- function(x) format(round(x, 3), big.mark=",")
gm <- list(
  list("raw" = "nobs", "clean" = "N", "fmt" = f),
  list("raw" = "AIC", "clean" = "aic", "fmt" = f))
modelsummary(models, gof_map = gm)

Notice the subtle difference between coef_map and gof_map. On the one hand, coef_map works as a “white list”: any coefficient not explicitly entered will be omitted from the table. On the other, gof_map works as a “black list”: statistics need to be explicitly marked for omission.

Another convenient way to build a gof_map argument is to use the tribble function from the tibble package. In this example, we insert special HTML code to display a superscript, so we use the escape=FALSE argument:

gm <- tibble::tribble(
  ~raw,        ~clean,          ~fmt,
  "nobs",      "N",             0,
  "r.squared", "R<sup>2</sup>", 2)

modelsummary(
  models,
  statistic = NULL,
  gof_map = gm,
  escape = FALSE)

	OLS 1	Poisson 1	OLS 2	Poisson 2	OLS 3
(Intercept)	7948.667	8.241	16259.384	9.876	11243.544
Literacy	-39.121	0.003	3.680	0.000	-68.507
Clergy	15.257		77.148		-16.376
Commerce		0.011		0.001
N	86	86	86	86	86
R<sup>2</sup>	0.02		0.07		0.15

`shape`: pivot, groups, panels, and stacks

This section requires version 1.3.1 of modelsummary. If this version is not available on CRAN yet, you can install the development version by following the instructions on the website.

The shape argument accepts:

A formula which determines the structure of the table, and can display “grouped” coefficients together (e.g., multivariate outcome or mixed-effects models).
The strings “rbind” or “rcollapse” to stack multiple tables on top of each other and present models in distinct “panels”.

Formula

The left side of the formula represents the rows and the right side represents the columns. The default formula is term + statistic ~ model:

m <- list(
    lm(mpg ~ hp, data = mtcars),
    lm(mpg ~ hp + drat, data = mtcars))

modelsummary(m, shape = term + statistic ~ model, gof_map = NA)

	(1)	(2)
(Intercept)	30.099	10.790
	(1.634)	(5.078)
hp	-0.068	-0.052
	(0.010)	(0.009)
drat		4.698
		(1.192)

We can display statistics horizontally with:

modelsummary(m,
             shape = term ~ model + statistic,
             statistic = "conf.int",
             gof_map = NA)

	(1)			(2)
	Est.	2.5 %	97.5 %	Est.	2.5 %	97.5 %
(Intercept)	30.099	26.762	33.436	10.790	0.405	21.175
hp	-0.068	-0.089	-0.048	-0.052	-0.071	-0.033
drat				4.698	2.261	7.135

The order of terms in the formula determines the order of headers in the table.

modelsummary(m,
             shape = term ~ statistic + model,
             statistic = "conf.int",
             gof_map = NA)

	Est.		2.5 %		97.5 %
	(1)	(2)	(1)	(2)	(1)	(2)
(Intercept)	30.099	10.790	26.762	0.405	33.436	21.175
hp	-0.068	-0.052	-0.089	-0.071	-0.048	-0.033
drat		4.698		2.261		7.135

shape does partial matching and will try to fill-in incomplete formulas:

modelsummary(m, shape = ~ statistic)

Some models like multinomial logit or GAMLSS produce “grouped” parameter estimates. To display these groups, we can include a group identifier in the shape formula. This group identifier must be one of the column names produced by get_estimates(model). For example, in models produced by nnet::multinom, the group identifier is called “response”:

library(nnet)

dat_multinom <- mtcars
dat_multinom$cyl <- sprintf("Cyl: %s", dat_multinom$cyl)

mod <- list(
    nnet::multinom(cyl ~ mpg, data = dat_multinom, trace = FALSE),
    nnet::multinom(cyl ~ mpg + drat, data = dat_multinom, trace = FALSE))

get_estimates(mod[[1]])
#>          term  estimate std.error conf.level   conf.low    conf.high statistic
#> 1 (Intercept) 47.252432 34.975171       0.95 -24.390958 118.89582104  1.351028
#> 2         mpg -2.205418  1.637963       0.95  -5.560632   1.14979649 -1.346440
#> 3 (Intercept) 72.440246 37.175162       0.95  -3.709622 148.59011342  1.948619
#> 4         mpg -3.579991  1.774693       0.95  -7.215284   0.05530216 -2.017246
#>   df.error    p.value response s.value group
#> 1       28 0.18750348   Cyl: 6     2.4      
#> 2       28 0.18896007   Cyl: 6     2.4      
#> 3       28 0.06142602   Cyl: 8     4.0      
#> 4       28 0.05334849   Cyl: 8     4.2

To summarize the results, we can type:

modelsummary(mod, shape = term + response ~ statistic)

	response	(1)		(2)
	response	Est.	S.E.	Est.	S.E.
(Intercept)	Cyl: 6	47.252	34.975	89.573	86.884
	Cyl: 8	72.440	37.175	117.971	87.998
mpg	Cyl: 6	-2.205	1.638	-3.627	3.869
	Cyl: 8	-3.580	1.775	-4.838	3.915
drat	Cyl: 6			-3.210	3.810
	Cyl: 8			-5.028	4.199
Num.Obs.		32		32
R2		0.763		0.815
R2 Adj.		0.733		0.786
AIC		24.1		24.5
BIC		30.0		33.3
RMSE		0.24		0.20

The terms of the shape formula above can of course be rearranged to reshape the table. For example:

modelsummary(mod, shape = model + term ~ response)

		Cyl: 6	Cyl: 8
(1)	(Intercept)	47.252	72.440
		(34.975)	(37.175)
	mpg	-2.205	-3.580
		(1.638)	(1.775)
(2)	(Intercept)	89.573	117.971
		(86.884)	(87.998)
	mpg	-3.627	-4.838
		(3.869)	(3.915)
	drat	-3.210	-5.028
		(3.810)	(4.199)

In version 1.0.1 of the package and later, we can combine the term and group identifier columns by inserting an interaction colon : instead of the + in the formula:

library(marginaleffects)
mod <- glm(am ~ mpg + factor(cyl), family = binomial, data = mtcars)
mfx <- marginaleffects(mod)

modelsummary(mfx, shape = term + contrast ~ model)

		(1)
cyl	mean(6) - mean(4)	0.097
		(0.166)
	mean(8) - mean(4)	0.093
		(0.234)
mpg	mean(dY/dX)	0.056
		(0.027)
Num.Obs.		32
AIC		37.4
BIC		43.3
Log.Lik.		-14.702
F		2.236
RMSE		0.39

modelsummary(mfx, shape = term : contrast ~ model)

	(1)
cyl mean(6) - mean(4)	0.097
	(0.166)
cyl mean(8) - mean(4)	0.093
	(0.234)
mpg mean(dY/dX)	0.056
	(0.027)
Num.Obs.	32
AIC	37.4
BIC	43.3
Log.Lik.	-14.702
F	2.236
RMSE	0.39

String (“rbind” or “rcollapse”): Panels of models in stacked regression tables

Note: The code in this section requires version 1.3.0 or the development version of modelsummary. See the website for installation instructions.

This section shows how to “stack/bind” multiple regression tables on top of one another, to display the results several models side-by-side and top-to-bottom. For example, imagine that we want to present 4 different models, half of which are estimated using a different outcome variable. When using modelsummary, we store models in a list. When using modelsummary with shape="rbind" or shape="rbind", we store models in a list of lists:

gm <- c("r.squared", "nobs", "rmse")

panels <- list(
  list(
    lm(mpg ~ 1, data = mtcars),
    lm(mpg ~ qsec, data = mtcars)
  ),
  list(
    lm(hp ~ 1, data = mtcars),
    lm(hp ~ qsec, data = mtcars)
  )
)

modelsummary(
  panels,
  shape = "rbind",
  gof_map = gm)

	(1)	(2)
Panel A
(Intercept)	20.091	-5.114
	(1.065)	(10.030)
qsec		1.412
		(0.559)
R2	0.000	0.175
Num.Obs.	32	32
RMSE	5.93	5.39
Panel B
(Intercept)	146.687	631.704
	(12.120)	(88.700)
qsec		-27.174
		(4.946)
R2	0.000	0.502
Num.Obs.	32	32
RMSE	67.48	47.64

Like with modelsummary(), we can can name models and panels by naming elements of our nested list:

panels <- list(
  "Outcome: mpg" = list(
    "(I)" = lm(mpg ~ 1, data = mtcars),
    "(II)" = lm(mpg ~ qsec, data = mtcars)
  ),
  "Outcome: hp" = list(
    "(I)" = lm(hp ~ 1, data = mtcars),
    "(II)" = lm(hp ~ qsec, data = mtcars)
  )
)

modelsummary(
  panels,
  shape = "rbind",
  gof_map = gm)

	(I)	(II)
Outcome: mpg
(Intercept)	20.091	-5.114
	(1.065)	(10.030)
qsec		1.412
		(0.559)
R2	0.000	0.175
Num.Obs.	32	32
RMSE	5.93	5.39
Outcome: hp
(Intercept)	146.687	631.704
	(12.120)	(88.700)
qsec		-27.174
		(4.946)
R2	0.000	0.502
Num.Obs.	32	32
RMSE	67.48	47.64

`fixest`

The fixest package offers powerful tools to estimate multiple models using a concise syntax. fixest functions are also convenient because they return named lists of models which are easy to subset and manipulate using standard R functions like grepl.

For example, to introduce regressors in stepwise fashion, and to estimate models on different subsets of the data, we can do:

# estimate 4 models
library(fixest)
mod <- feols(
  c(hp, mpg) ~ csw(qsec, drat) | gear,
  data = mtcars)

# select models with different outcome variables
panels <- list(
  "Miles per gallon" = mod[grepl("mpg", names(mod))],
  "Horsepower" = mod[grepl("hp", names(mod))]
)

modelsummary(
  panels,
  shape = "rcollapse",
  gof_omit = "IC|R2")

	(1)	(2)
Miles per gallon
qsec	1.436	1.519
	(0.594)	(0.529)
drat		5.765
		(2.381)
RMSE	4.03	3.67
Horsepower
qsec	-22.175	-22.676
	(12.762)	(13.004)
drat		-35.106
		(28.509)
RMSE	40.45	39.14

Num.Obs.	32	32
Std.Errors	by: gear	by: gear
FE: gear	X	X

We can use all the typical extension systems to add information, such as the mean of the dependent variable:

glance_custom.fixest <- function(x, ...) {
  dv <- insight::get_response(x)
  dv <- sprintf("%.2f", mean(dv, na.rm = TRUE))
  data.table::data.table(`Mean of DV` = dv)
}

modelsummary(
  panels,
  shape = "rcollapse",
  gof_omit = "IC|R2")

	(1)	(2)
Miles per gallon
qsec	1.436	1.519
	(0.594)	(0.529)
drat		5.765
		(2.381)
RMSE	4.03	3.67
Mean of DV	20.09	20.09
Horsepower
qsec	-22.175	-22.676
	(12.762)	(13.004)
drat		-35.106
		(28.509)
RMSE	40.45	39.14
Mean of DV	146.69	146.69

Num.Obs.	32	32
Std.Errors	by: gear	by: gear
FE: gear	X	X


rm("glance_custom.fixest")

`align`: column alignment

By default, modelsummary will align the first column (with coefficient names) to the left, and will center the results columns. To change this default, you can use the align argument, which accepts a string of the same length as the number of columns:

modelsummary(models, align="lrrrrr")

Users who produce PDF documents using Rmarkdown or LaTeX can also align values on the decimal dot by using the character “d” in the align argument:

modelsummary(models, align="lddddd")

For the table produced by this code to compile, users must include the following code in their LaTeX preamble:

\usepackage{booktabs}
\usepackage{siunitx}
\newcolumntype{d}{S[input-symbols = ()]}

`notes`: footers

Add notes to the bottom of your table:

modelsummary(models, 
   notes = list('Text of the first note.', 
                'Text of the second note.'))

`title`: captions

You can add a title to your table as follows:

modelsummary(models, title = 'This is a title for my table.')

`add_rows`

Use the add_rows argument to add rows manually to a table. For example, let’s say you estimate two models with a factor variables and you want to insert (a) an empty line to identify the category of reference, and (b) customized information at the bottom of the table:

models <- list()
models[['OLS']] <- lm(mpg ~ factor(cyl), mtcars)
models[['Logit']] <- glm(am ~ factor(cyl), mtcars, family = binomial)

We create a data.frame with the same number of columns as the summary table. Then, we define a “position” attribute to specify where the new rows should be inserted in the table. Finally, we pass this data.frame to the add_rows argument:

library(tibble)
rows <- tribble(~term,          ~OLS,  ~Logit,
                'factor(cyl)4', '-',   '-',
                'Info',         '???', 'XYZ')
attr(rows, 'position') <- c(3, 9)

modelsummary(models, add_rows = rows)

	OLS	Logit
(Intercept)	26.664	0.981
	(0.972)	(0.677)
factor(cyl)4	-	-
factor(cyl)6	-6.921	-1.269
	(1.558)	(1.021)
factor(cyl)8	-11.564	-2.773
	(1.299)	(1.021)
Num.Obs.	32	32
Info	???	XYZ
R2	0.732
R2 Adj.	0.714
AIC	170.6	39.9
BIC	176.4	44.3
Log.Lik.	-81.282	-16.967
F	39.698	3.691
RMSE	3.07	0.42

`exponentiate`

We can exponentiate their estimates using the exponentiate argument:

mod_logit <- glm(am ~ mpg, data = mtcars, family = binomial)
modelsummary(mod_logit, exponentiate = TRUE)

	(1)
(Intercept)	0.001
	(0.003)
mpg	1.359
	(0.156)
Num.Obs.	32
AIC	33.7
BIC	36.6
Log.Lik.	-14.838
F	7.148
RMSE	0.39

We can also present exponentiated and standard models side by side by using a logical vector:

mod_logit <- list(mod_logit, mod_logit)
modelsummary(mod_logit, exponentiate = c(TRUE, FALSE))

	(1)	(2)
(Intercept)	0.001	-6.604
	(0.003)	(2.351)
mpg	1.359	0.307
	(0.156)	(0.115)
Num.Obs.	32	32
AIC	33.7	33.7
BIC	36.6	36.6
Log.Lik.	-14.838	-14.838
F	7.148	7.148
RMSE	0.39	0.39

`...` ellipsis: Additional arguments

All arguments passed by the user to a modelsummary function are pushed forward in two other functions:

The function which extracts model estimates.
- By default, additional arguments are pushed forward to parameters::parameters and performance::performance. Users can also can also use a different “backend” to extract information from model objects: the broom package. By setting the modelsummary_get global option, we tell modelsummary to use the easystats/parameters packages instead of broom. With these packages, other arguments are available, such as the metrics argument. Please refer to these package’s documentation to details.
The table-making functions.
- By default, additional arguments are pushed forward to kableExtra::kbl, but users can use a different table-making function by setting the output argument to a different value such as "gt", "flextable", or "huxtable".
- See the Appearance vignette for examples.

All arguments passed supported by these functions are thus automatically available directly in modelsummary, modelplot, and the datasummary family of functions.

Custom appearance

To customize the appearance of tables, modelsummary supports five popular and extremely powerful table-making packages:

The “customizing the look of your tables” vignette shows examples for all 4 packages.

Supported models

modelsummary automatically supports all the models supported by the tidy function of the broom package or the parameters function of the parameters package. The list of supported models is rapidly expanding. At the moment, it covers the following model classes:

supported_models()
#>   [1] "aareg"                    "acf"                     
#>   [3] "afex_aov"                 "AKP"                     
#>   [5] "anova"                    "Anova.mlm"               
#>   [7] "anova.rms"                "aov"                     
#>   [9] "aovlist"                  "Arima"                   
#>  [11] "averaging"                "bamlss"                  
#>  [13] "bayesQR"                  "bcplm"                   
#>  [15] "befa"                     "betamfx"                 
#>  [17] "betaor"                   "betareg"                 
#>  [19] "BFBayesFactor"            "bfsl"                    
#>  [21] "BGGM"                     "bifeAPEs"                
#>  [23] "biglm"                    "binDesign"               
#>  [25] "binWidth"                 "blavaan"                 
#>  [27] "blrm"                     "boot"                    
#>  [29] "bracl"                    "brmsfit"                 
#>  [31] "brmultinom"               "btergm"                  
#>  [33] "cch"                      "censReg"                 
#>  [35] "cgam"                     "character"               
#>  [37] "cld"                      "clm"                     
#>  [39] "clm2"                     "clmm"                    
#>  [41] "clmm2"                    "coeftest"                
#>  [43] "comparisons"              "confint.glht"            
#>  [45] "confusionMatrix"          "coxph"                   
#>  [47] "cpglmm"                   "crr"                     
#>  [49] "cv.glmnet"                "data.frame"              
#>  [51] "dbscan"                   "default"                 
#>  [53] "deltaMethod"              "density"                 
#>  [55] "dep.effect"               "DirichletRegModel"       
#>  [57] "dist"                     "draws"                   
#>  [59] "drc"                      "durbinWatsonTest"        
#>  [61] "emm_list"                 "emmeans"                 
#>  [63] "emmeans_summary"          "emmGrid"                 
#>  [65] "epi.2by2"                 "ergm"                    
#>  [67] "fa"                       "fa.ci"                   
#>  [69] "factanal"                 "FAMD"                    
#>  [71] "feglm"                    "felm"                    
#>  [73] "fitdistr"                 "fixest"                  
#>  [75] "fixest_multi"             "flac"                    
#>  [77] "flic"                     "ftable"                  
#>  [79] "gam"                      "Gam"                     
#>  [81] "gamlss"                   "gamm"                    
#>  [83] "garch"                    "geeglm"                  
#>  [85] "ggeffects"                "glht"                    
#>  [87] "glimML"                   "glm"                     
#>  [89] "glmm"                     "glmmTMB"                 
#>  [91] "glmnet"                   "glmrob"                  
#>  [93] "glmRob"                   "glmx"                    
#>  [95] "gmm"                      "hclust"                  
#>  [97] "hdbscan"                  "hglm"                    
#>  [99] "hkmeans"                  "HLfit"                   
#> [101] "htest"                    "hurdle"                  
#> [103] "hypotheses"               "irlba"                   
#> [105] "ivFixed"                  "ivprobit"                
#> [107] "ivreg"                    "kappa"                   
#> [109] "kde"                      "Kendall"                 
#> [111] "kmeans"                   "lavaan"                  
#> [113] "leveneTest"               "Line"                    
#> [115] "Lines"                    "list"                    
#> [117] "lm"                       "lm_robust"               
#> [119] "lm.beta"                  "lme"                     
#> [121] "lmodel2"                  "lmrob"                   
#> [123] "lmRob"                    "logical"                 
#> [125] "logistf"                  "logitmfx"                
#> [127] "logitor"                  "lqm"                     
#> [129] "lqmm"                     "lsmobj"                  
#> [131] "manova"                   "maov"                    
#> [133] "map"                      "marginaleffects"         
#> [135] "marginalmeans"            "margins"                 
#> [137] "maxim"                    "maxLik"                  
#> [139] "mblogit"                  "Mclust"                  
#> [141] "mcmc"                     "mcmc.list"               
#> [143] "MCMCglmm"                 "mcp1"                    
#> [145] "mcp2"                     "med1way"                 
#> [147] "mediate"                  "merMod"                  
#> [149] "merModList"               "meta_bma"                
#> [151] "meta_fixed"               "meta_random"             
#> [153] "metaplus"                 "mfx"                     
#> [155] "mhurdle"                  "mipo"                    
#> [157] "mira"                     "mixed"                   
#> [159] "MixMod"                   "mixor"                   
#> [161] "mjoint"                   "mle"                     
#> [163] "mle2"                     "mlm"                     
#> [165] "mlogit"                   "mmrm"                    
#> [167] "mmrm_fit"                 "mmrm_tmb"                
#> [169] "model_fit"                "model_parameters"        
#> [171] "muhaz"                    "multinom"                
#> [173] "mvord"                    "negbin"                  
#> [175] "negbinirr"                "negbinmfx"               
#> [177] "nestedLogit"              "nlrq"                    
#> [179] "nls"                      "NULL"                    
#> [181] "numeric"                  "omega"                   
#> [183] "onesampb"                 "optim"                   
#> [185] "orcutt"                   "osrt"                    
#> [187] "pairwise.htest"           "pam"                     
#> [189] "parameters_efa"           "parameters_pca"          
#> [191] "PCA"                      "pgmm"                    
#> [193] "plm"                      "PMCMR"                   
#> [195] "poissonirr"               "poissonmfx"              
#> [197] "poLCA"                    "polr"                    
#> [199] "Polygon"                  "Polygons"                
#> [201] "power.htest"              "prcomp"                  
#> [203] "predictions"              "principal"               
#> [205] "probitmfx"                "pvclust"                 
#> [207] "pyears"                   "rcorr"                   
#> [209] "ref.grid"                 "regsubsets"              
#> [211] "ridgelm"                  "rlm"                     
#> [213] "rlmerMod"                 "rma"                     
#> [215] "robtab"                   "roc"                     
#> [217] "rq"                       "rqs"                     
#> [219] "rqss"                     "sarlm"                   
#> [221] "Sarlm"                    "scam"                    
#> [223] "selection"                "sem"                     
#> [225] "SemiParBIV"               "slopes"                  
#> [227] "SpatialLinesDataFrame"    "SpatialPolygons"         
#> [229] "SpatialPolygonsDataFrame" "spec"                    
#> [231] "speedglm"                 "speedlm"                 
#> [233] "stanfit"                  "stanmvreg"               
#> [235] "stanreg"                  "summary_emm"             
#> [237] "summary.glht"             "summary.lm"              
#> [239] "summary.plm"              "summaryDefault"          
#> [241] "survdiff"                 "survexp"                 
#> [243] "survfit"                  "survreg"                 
#> [245] "svd"                      "svyglm"                  
#> [247] "svyolr"                   "svytable"                
#> [249] "systemfit"                "t1way"                   
#> [251] "table"                    "tobit"                   
#> [253] "trendPMCMR"               "trimcibt"                
#> [255] "ts"                       "TukeyHSD"                
#> [257] "varest"                   "vgam"                    
#> [259] "wbgee"                    "wbm"                     
#> [261] "wmcpAKP"                  "xyz"                     
#> [263] "yuen"                     "zcpglm"                  
#> [265] "zerocount"                "zeroinfl"                
#> [267] "zoo"

To see if a given model is supported, you can fit it, and then call this function:

get_estimates(model)

If this function does not return a valid output, you can easily (really!!) add your own support. See the next section for a tutorial. If you do this, you may consider opening an issue on the Github website of the broom package: https://github.com/tidymodels/broom/issues

New models and custom statistics

Adding new models (part I)

The simplest way to summarize an unsupported model is to create a modelsummary_list object. This approach is super flexible, but it requires manual intervention, and it can become tedious if you need to summarize many models. The next section shows how to add formal support for an unsupported model type.

A modelsummary_list is a list with two element that conform to the broom package specification: tidy and glance. tidy is a data.frame with at least three columns: term, estimate, and std.error. glance is a data.frame with only a single row, and where each column will be displayed at the bottom of the table in the goodness-of-fit section. Finally, we wrap those two elements in a list and assign it a modelsummary_list class:

ti <- data.frame(
  term = c("coef1", "coef2", "coef3"),
  estimate = 1:3,
  std.error = c(pi, exp(1), sqrt(2)))

gl <- data.frame(
  stat1 = "blah",
  stat2 = "blah blah")

mod <- list(
  tidy = ti,
  glance = gl)
class(mod) <- "modelsummary_list"

modelsummary(mod)

	(1)
coef1	1.000
	(3.142)
coef2	2.000
	(2.718)
coef3	3.000
	(1.414)
stat1	blah
stat2	blah blah

Adding new models (part II)

modelsummary relies on two functions from the broom package to extract model information: tidy and glance. If broom doesn’t support the type of model you are trying to summarize, modelsummary won’t support it out of the box. Thankfully, it is extremely easy to add support for most models using custom methods.

For example, models produced by the MCMCglmm package are not currently supported by broom. To add support, you simply need to create a tidy and a glance method:

# load packages and data
library(modelsummary)
library(MCMCglmm)
data(PlodiaPO)

# add custom functions to extract estimates (tidy) and goodness-of-fit (glance) information
tidy.MCMCglmm <- function(x, ...) {
    s <- summary(x, ...)
    ret <- data.frame(
      term      = row.names(s$solutions),
      estimate  = s$solutions[, 1],
      conf.low  = s$solutions[, 2],
      conf.high = s$solutions[, 3])
    ret
}

glance.MCMCglmm <- function(x, ...) {
    ret <- data.frame(
      dic = x$DIC,
      n   = nrow(x$X))
    ret
}

# estimate a simple model
model <- MCMCglmm(PO ~ 1 + plate, random = ~ FSfamily, data = PlodiaPO, verbose=FALSE, pr=TRUE)

# summarize the model
modelsummary(model, statistic = 'conf.int')

Three important things to note.

First, the methods are named tidy.MCMCglmm and glance.MCMCglmm because the model object I am trying to summarize is of class MCMCglmm. You can find the class of a model by running: class(model).

Second, both of the methods include the ellipsis ... argument.

Third, in the example above we used the statistic = 'conf.int' argument. This is because the tidy method produces conf.low and conf.high columns. In most cases, users will define std.error column in their custom tidy methods, so the statistic argument will need to be adjusted.

If you create new tidy and glance methods, please consider contributing them to broom so that the rest of the community can benefit from your work: https://github.com/tidymodels/broom

Adding new information to existing models

Users may want to include more information than is made available by the default extractor function. For example, models produced by the MASS::polr do not produce p values by default, which means that we cannot use the stars=TRUE argument in modelsummary. However, it is possible to extract this information by using the lmtest::coeftest function. To include such custom information, we will define new glance_custom and tidy_custom methods.

We begin by estimating a model with the MASS::polr:

library(MASS)

mod_ordinal <- polr(as.ordered(gear) ~ mpg + drat, data = mtcars)

get_estimates(mod_ordinal)
#>   term     estimate  std.error conf.level   conf.low  conf.high  statistic
#> 1  3|4 13.962948762 4.04107299       0.95  5.6851860 22.2407115  3.4552578
#> 2  4|5 16.898937339 4.39497069       0.95  7.8962480 25.9016267  3.8450626
#> 3  mpg -0.008646679 0.09034201       0.95 -0.1916706  0.1708667 -0.0957105
#> 4 drat  3.949431922 1.30665144       0.95  1.6191505  6.8457246  3.0225597
#>   df.error      p.value component s.value group
#> 1       28 0.0017706302     alpha     9.1      
#> 2       28 0.0006356348     alpha    10.6      
#> 3       28 0.9244322337      beta     0.1      
#> 4       28 0.0053120619      beta     7.6

The get_estimates function shows that our default extractor does not produce a p.value column. As a result, setting stars=TRUE in modelsummary will produce an error.

We know that the MASS::polr produces an object of class polr:

class(mod_ordinal)
#> [1] "polr"

To extract more (custom) information from a model of this class, we thus define a method called tidy_custom.polr which returns a data.frame with two columns: term and p.value:

tidy_custom.polr <- function(x, ...) {
  s <- lmtest::coeftest(x)
  out <- data.frame(
    term = row.names(s),
    p.value = s[, "Pr(>|t|)"])
  out
}

When this method is defined, modelsummary can automatically extract p values from all models of this class, and will now work properly with stars=TRUE:

modelsummary(mod_ordinal, stars = TRUE)

	(1)
3\|4	13.963**
	(4.041)
4\|5	16.899***
	(4.395)
mpg	-0.009
	(0.090)
drat	3.949**
	(1.307)
Num.Obs.	32
AIC	51.1
BIC	57.0
RMSE	3.44
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001

Customizing existing models (part I)

Sometimes users will want to include information that is not supplied by those functions. A pretty easy way to include extra information is to define new glance_custom and tidy_custom methods. To illustrate, we estimate two linear regression models using the lm function:

library(modelsummary)

mod <- list()
mod[[1]] <- lm(hp ~ mpg + drat, mtcars)
mod[[2]] <- lm(wt ~ mpg + drat + am, mtcars)

In R, the lm function produces models of class “lm”:

class(mod[[1]])
#> [1] "lm"

Let’s say you would like to print the dependent variable for each model of this particular class. All you need to do is define a new method called glance_custom.lm. This method should return a data.frame (or tibble) with 1 row, and 1 column per piece of information you want to display. For example:

glance_custom.lm <- function(x, ...) {
    dv <- as.character(formula(x)[2])
    out <- data.frame("DV" = dv)
    return(out)
}

Now, let’s customize the body of the table. The vcov argument already allows users to customize uncertainty estimates. But imagine you want to override the coefficient estimates of your “lm” models. Easy! All you need to do is define a tidy_custom.lm method which returns a data.frame (or tibble) with one column called “term” and one column called “estimate”.

Here, we’ll substitute estimates by an up/down-pointing triangles which represents their signs:

tidy_custom.lm <- function(x, ...) {
    s <- summary(x)$coefficients
    out <- data.frame(
      term = row.names(s),
      estimate = ifelse(s[,1] > 0, '▲', '▼'))
    return(out)
}

After you define the glance_custom and tidy_custom methods, modelsummary will automatically display your customized model information:

modelsummary(mod)

	(1)	(2)
(Intercept)	▲	▲
	(55.415)	(0.728)
mpg	▼	▼
	(1.792)	(0.019)
drat	▲	▼
	(20.198)	(0.245)
am		▼
		(0.240)
Num.Obs.	32	32
R2	0.614	0.803
R2 Adj.	0.588	0.782
AIC	337.9	46.4
BIC	343.7	53.7
Log.Lik.	-164.940	-18.201
F	23.100	38.066
RMSE	41.91	0.43
DV	hp	wt

Note that you can define a std.error column in tidy_custom.lm to replace the uncertainty estimates instead of the coefficients.

Customizing existing models (part II)

An even more fundamental way to customize the output would be to completely bypass modelsummary’s extractor functions by assigning a new class name to your model. For example,

# estimate a linear model
mod_custom <- lm(hp ~ mpg + drat, mtcars)

# assign it a new class
class(mod_custom) <- "custom"

# define tidy and glance methods
tidy.custom <- function(x, ...) {
  data.frame(
    term = names(coef(x)),
    estimate = letters[1:length(coef(x))],
    std.error = seq_along(coef(x))
  )
}

glance.custom <- function(x, ...) {
  data.frame(
    "Model" = "Custom",
    "nobs" = stats:::nobs.lm(x)
  )
}

# summarize
modelsummary(mod_custom)

	(1)
(Intercept)	a
	(1.000)
mpg	b
	(2.000)
drat	c
	(3.000)
Num.Obs.	32
Model	Custom

Warning: When defining new tidy and glance methods, it is important to include an ellipsis argument (...).

Note that in the glance.custom() method, we called stats:::nobs.lm() instead of the default stats::nobs() method, because the latter know does not know where to dispatch models of our new “custom” class. Being more explicit solves the problem.

An alternative would be to set a new class that inherits from the previous one, and to use a global option to set broom as the default extractor function (otherwise modelsummary will use its standard lm extractors by inheritance):

options(modelsummary_get = "broom")
class(mod_custom) <- c("custom", "lm")

Customizing existing models (part III)

Another flexible way to customize model output is to use output = "modelsummary_list". With this output option, modelsummary() returns a list with two elements: tidy contains parameter estimates, standard errors, etc., and glance contains model statistics such as the AIC. For example,

mod <- lm(hp ~ mpg + drat, mtcars)
mod_list <- modelsummary(mod, output = "modelsummary_list")
mod_list$tidy
#>          term   estimate std.error  statistic df.error      p.value s.value
#> 1 (Intercept) 278.515455 55.414866  5.0260061       29 2.359726e-05    15.4
#> 2         mpg  -9.985499  1.791837 -5.5727709       29 5.172030e-06    17.6
#> 3        drat  19.125752 20.197756  0.9469246       29 3.515013e-01     1.5
#>   group conf.low conf.high
#> 1             NA        NA
#> 2             NA        NA
#> 3             NA        NA
mod_list$glance
#>        aic      bic r.squared adj.r.squared     rmse nobs        F    logLik
#> 1 337.8809 343.7438 0.6143611     0.5877653 41.90687   32 23.09994 -164.9404

Both tidy and glance can now be customized, and the updated model can be passed back to modelsummary using modelsummary(mod_list). All information that is displayed in the table is contained in mod_list, so this pattern allows for very flexible adjustments of output tables.

A useful example for this pattern concerns mixed models using lme4. Assume we want to compare the effect of using different degrees-of-freedom adjustments on the significance of the coefficients. The models have identical parameter estimates, standard errors, and model fit statistics - we only want to change the p-values. We use the parameters package to compute the adjusted p-values.

library("lme4")
mod <- lmer(mpg ~ drat + (1 | am), data = mtcars)
mod_list <- modelsummary(mod, output = "modelsummary_list", effects = "fixed")
# create a copy, where we'll change the p-values
mod_list_kenward <- as.list(mod_list)
mod_list_kenward$tidy$p.value <- parameters::p_value_kenward(mod)$p

modelsummary(list("Wald" = mod_list, "Kenward" = mod_list_kenward), 
            statistic = "{std.error} ({p.value}) {stars}")

	Wald	Kenward
(Intercept)	-5.159	-5.159
	6.409 (0.428)	6.409 (0.680)
drat	7.045	7.045
	1.736 (<0.001) ***	1.736 (0.086) +
Num.Obs.	32	32
R2 Marg.	0.402	0.402
R2 Cond.	0.440	0.440
AIC	188.7	188.7
BIC	194.6	194.6
ICC	0.1	0.1
RMSE	4.28	4.28

Rmarkdown, Quarto, Org-Mode

Rmarkdown

You can use modelsummary to insert tables into dynamic documents with knitr or Rmarkdown. This minimal .Rmd file can produce tables in PDF, HTML, or RTF documents:

minimal.Rmd

This .Rmd file shows illustrates how to use table numbering and cross-references to produce PDF documents using bookdown:

cross_references.Rmd

This .Rmd file shows how to customize tables in PDF and HTML files using gt and kableExtra functions:

appearance.Rmd

Quarto

Quarto is an open source publishing system built on top of Pandoc. It was designed as a “successor” to Rmarkdown, and includes useful features for technical writing, such as built-in support for cross-references. modelsummary works automatically with Quarto. This is a minimal document with cross-references which should render automatically to PDF, HTML, and more:

---
format: pdf
title: Example
---

@tbl-mtcars shows that cars with high horse power get low miles per gallon.

```{r}
#| label: tbl-mtcars
#| tbl-cap: "Horse Powers vs. Miles per Gallon"
library(modelsummary)
mod <- lm(mpg ~ hp, mtcars)
modelsummary(mod)
```

Emacs Org-Mode

You can use modelsummary to insert tables into Emacs Org-Mode documents, which can be exported to a variety of formats, including HTML and PDF (via LaTeX). As with anything Emacs-related, there are many ways to achieve the outcomes you want. Here is one example of an Org-Mode document which can automatically export tables to HTML and PDF without manual tweaks:

#+PROPERTY: header-args:R :var orgbackend=(prin1-to-string org-export-current-backend)
#+MACRO: Rtable (eval (concat "#+header: :results output " (prin1-to-string org-export-current-backend)))

{{{Rtable}}}
#+BEGIN_SRC R :exports both
library(modelsummary)
options(modelsummary_factory_default = orgbackend)

mod = lm(hp ~ mpg, data = mtcars)

modelsummary(mod)
#+END_SRC

The first line tells Org-mode to assign a variable called orgbackend. This variable will be accessible by the R session, and will be equal to “html” or “latex”, depending on the export format.

The second line creates an Org macro which we will use to automatically add useful information to the header of source blocks. For instance, when we export to HTML, the macro will expand to :results output html. This tells Org-Mode to insert the last printed output from the R session, and to treat it as raw HTML.

The {{{Rtable}}} call expands the macro to add information to the header of the block that follows.

#+BEGIN_SRC R :exports both says that we want to print both the original code and the output (:exports results would omit the code, for example).

Finally, options(modelsummary_factory_default=orgbackend uses the variable we defined to set the default output format. That way, we don’t have to use the output argument every time.

One potentially issue to keep in mind is that the code above extracts the printout from the R console. However, when we customize tables with kableExtra or gt functions, those functions do not always return printed raw HTML or LaTeX code. Sometimes, it can be necessary to add a call to cat at the end of a table customization pipeline. For example:

{{{Rtable}}}
#+BEGIN_SRC R :exports both
library(modelsummary)
library(kableExtra)

mod = lm(hp ~ mpg, data = mtcars)

modelsummary(mod, output = orgbackend) %>%
  row_spec(1, background = "pink") %>%
  cat()
#+END_SRC

Global options

Users can change the default behavior of modelsummary by setting global options.

Omit the note at the bottom of the table with significance threshold:

options("modelsummary_stars_note" = FALSE)

Change the default output format:

options(modelsummary_factory_default = "latex")
options(modelsummary_factory_default = "gt")

Change the backend packages that modelsummary uses to create tables in different output formats:

options(modelsummary_factory_html = 'kableExtra')
options(modelsummary_factory_latex = 'flextable')
options(modelsummary_factory_word = 'huxtable')
options(modelsummary_factory_png = 'gt')

Change the packages that modelsummary uses to extract information from models:

# tidymodels: broom 
options(modelsummary_get = "broom")

# easystats: performance + parameters
options(modelsummary_get = "easystats")

The appearance vignette shows how to set “themes” for your tables using the modelsummary_theme_gt, modelsummary_theme_kableExtra, modelsummary_theme_flextable and modelsummary_theme_huxtable global options. For example:

library(gt)

# The ... ellipsis is required!
custom_theme <- function(x, ...) {
    x %>% gt::opt_row_striping(row_striping = TRUE)
}
options("modelsummary_theme_gt" = custom_theme)

mod <- lm(mpg ~ hp + drat, mtcars)
modelsummary(mod, output = "gt")

Case studies

Subgroup estimation with `nest_by`

Sometimes, it is useful to estimate multiple regression models on subsets of the data. To do this efficiently, we can use the nest_by function from the dplyr package. Then, estimate the models with lm, extract them and name them with pull, and finally summarize them with modelsummary:

library(tidyverse)

mtcars %>%
    nest_by(cyl) %>%
    mutate(models = list(lm(mpg ~ hp, data))) %>%
    pull(models, name = cyl) %>%
    modelsummary

	4	6	8
(Intercept)	35.983	20.674	18.080
	(5.201)	(3.304)	(2.988)
hp	-0.113	-0.008	-0.014
	(0.061)	(0.027)	(0.014)
Num.Obs.	11	7	14
R2	0.274	0.016	0.080
R2 Adj.	0.193	-0.181	0.004
AIC	65.8	29.9	69.8
BIC	67.0	29.7	71.8
Log.Lik.	-29.891	-11.954	-31.920
RMSE	3.66	1.33	2.37

Statistics in separate columns instead of one over the other

In somes cases, you may want to display statistics in separate columns instead of one over the other. It is easy to achieve this outcome by using the estimate argument. This argument accepts a vector of values, one for each of the models we are trying to summarize. If we want to include estimates and standard errors in separate columns, all we need to do is repeat a model, but request different statistics. For example,

library(modelsummary)
library(kableExtra)

mod1 <- lm(mpg ~ hp, mtcars)
mod2 <- lm(mpg ~ hp + drat, mtcars)

models <- list(
    "Coef."     = mod1,
    "Std.Error" = mod1,
    "Coef."     = mod2,
    "Std.Error" = mod2)

modelsummary(models,
             estimate = c("estimate", "std.error", "estimate", "std.error"),
             statistic = NULL,
             gof_omit = ".*",
             output = "kableExtra") %>%
    add_header_above(c(" " = 1, "Model A" = 2, "Model B" = 2))

	Model A		Model B
	Coef.	Std.Error	Coef.	Std.Error
(Intercept)	30.099	1.634	10.790	5.078
hp	−0.068	0.010	−0.052	0.009
drat			4.698	1.192

This can be automated using a simple function:

side_by_side <- function(models, estimates, ...) {
    models <- rep(models, each = length(estimates))
    estimates <- rep(estimates, times = 2)
    names(models) <- names(estimates)
    modelsummary(models = models, estimate = estimates,
                 statistic = NULL, gof_omit = ".*", ...)
}

models = list(
    lm(mpg ~ hp, mtcars),
    lm(mpg ~ hp + drat, mtcars))

estimates <- c("Coef." = "estimate", "Std.Error" = "std.error")

side_by_side(models, estimates = estimates)

	Coef.	Std.Error	Coef.	Std.Error
(Intercept)	30.099	1.634	10.790	5.078
hp	-0.068	0.010	-0.052	0.009
drat			4.698	1.192

Bootstrap

Users often want to use estimates or standard errors that have been obtained using a custom strategy. To achieve this in an automated and replicable way, it can be useful to use the tidy_custom strategy described above in the “Cutomizing Existing Models” section.

For example, we can use the modelr package to draw 500 resamples of a dataset, and compute bootstrap standard errors by taking the standard deviation of estimates computed in all of those resampled datasets. To do this, we defined tidy_custom.lm function that will automatically bootstrap any lm model supplied to modelsummary, and replace the values in the table automatically.

Note that the tidy_custom_lm returns a data.frame with 3 columns: term, estimate, and std.error:

library("modelsummary")
library("broom")
library("tidyverse")
library("modelr")

tidy_custom.lm <- function(x, ...) {
  # extract data from the model
  model.frame(x) %>%
    # draw 500 bootstrap resamples
    modelr::bootstrap(n = 500) %>%
    # estimate the model 500 times
    mutate(results = map(strap, ~ update(x, data = .))) %>%
    # extract results using `broom::tidy`
    mutate(results = map(results, tidy)) %>%
    # unnest and summarize
    unnest(results) %>%
    group_by(term) %>%
    summarize(std.error = sd(estimate),
              estimate = mean(estimate))
}

mod = list(
  lm(hp ~ mpg, mtcars) ,
  lm(hp ~ mpg + drat, mtcars))

modelsummary(mod)

	(1)	(2)
(Intercept)	327.164	284.250
	(32.379)	(42.713)
mpg	-9.028	-9.942
	(1.469)	(2.364)
drat		17.110
		(21.662)
Num.Obs.	32	32
R2	0.602	0.614
R2 Adj.	0.589	0.588
AIC	336.9	337.9
BIC	341.3	343.7
Log.Lik.	-165.428	-164.940
F	45.460	23.100
RMSE	42.55	41.91

`fixest`: Fixed effects and instrumental variable regression

One common use-case for glance_custom is to include additional goodness-of-fit statistics. For example, in an instrumental variable estimation computed by the fixest package, we may want to include an IV-Wald statistic for the first-stage regression of each endogenous regressor:

library(fixest)
library(tidyverse)

# create a toy dataset
base <- iris
names(base) <- c("y", "x1", "x_endo_1", "x_inst_1", "fe")
base$x_inst_2 <- 0.2 * base$y + 0.2 * base$x_endo_1 + rnorm(150, sd = 0.5)
base$x_endo_2 <- 0.2 * base$y - 0.2 * base$x_inst_1 + rnorm(150, sd = 0.5)

# estimate an instrumental variable model
mod <- feols(y ~ x1 | fe | x_endo_1 + x_endo_2 ~ x_inst_1 + x_inst_2, base)

# custom extractor function returns a one-row data.frame (or tibble)
glance_custom.fixest <- function(x) {
  tibble(
    "Wald (x_endo_1)" = fitstat(x, "ivwald")[[1]]$stat,
    "Wald (x_endo_2)" = fitstat(x, "ivwald")[[2]]$stat
  )
}

# draw table
modelsummary(mod)

	(1)
fit_x_endo_1	47.308
	(6327.986)
fit_x_endo_2	985.740
	(134600.030)
x1	-160.580
	(21899.668)
Num.Obs.	150
R2	-360663.300
R2 Adj.	-373186.366
R2 Within	-945893.887
R2 Within Adj.	-965600.031
AIC	2299.4
BIC	2317.5
RMSE	495.64
Std.Errors	by: fe
FE: fe	X
Wald (x_endo_1)	12.8675873489982
Wald (x_endo_2)	0.059201450090191

rm("glance_custom.fixest")

Multiple imputation

modelsummary can pool and display analyses on several datasets imputed using the mice or Amelia packages. This code illustrates how:

library(mice)
library(Amelia)
library(modelsummary)

# Download data from `Rdatasets`
url <- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
dat <- read.csv(url)[, c('Clergy', 'Commerce', 'Literacy')]

# Insert missing values
dat$Clergy[sample(1:nrow(dat), 10)] <- NA
dat$Commerce[sample(1:nrow(dat), 10)] <- NA
dat$Literacy[sample(1:nrow(dat), 10)] <- NA

# Impute with `mice` and `Amelia`
dat_mice <- mice(dat, m = 5, printFlag = FALSE)
dat_amelia <- amelia(dat, m = 5, p2s = 0)$imputations

# Estimate models
mod <- list()
mod[['Listwise deletion']] <- lm(Clergy ~ Literacy + Commerce, dat)
mod[['Mice']] <- with(dat_mice, lm(Clergy ~ Literacy + Commerce)) 
mod[['Amelia']] <- lapply(dat_amelia, function(x) lm(Clergy ~ Literacy + Commerce, x))

# Pool results
mod[['Mice']] <- mice::pool(mod[['Mice']])
mod[['Amelia']] <- mice::pool(mod[['Amelia']])

# Summarize
modelsummary(mod)

	Listwise deletion	Mice	Amelia
(Intercept)	88.292	81.111	85.150
	(13.365)	(11.595)	(15.629)
Literacy	-0.521	-0.518	-0.539
	(0.210)	(0.192)	(0.226)
Commerce	-0.509	-0.400	-0.467
	(0.152)	(0.130)	(0.162)
Num.Obs.	60	86	86
Num.Imp.		5	5
R2	0.173	0.131	0.163
R2 Adj.	0.144	0.110	0.142
AIC	557.3
BIC	565.7
Log.Lik.	-274.646
RMSE	23.54

Table-making packages

The table-making backends supported by modelsummary have overlapping capabilities (e.g., several of them can produce HTML tables). These are the default packages used for different outputs:

kableExtra:

HTML
LaTeX / PDF

flextable:

Word
Powerpoint

gt:

You can modify these defaults by setting global options such as:

options(modelsummary_factory_html = "kableExtra")
options(modelsummary_factory_latex = "gt")
options(modelsummary_factory_word = "huxtable")
options(modelsummary_factory_png = "gt")

FAQ

Stack Overflow is useful

Where can I get help?

First, please read the documentation in ?modelsummary and on the modelsummary website. The website includes dozens of worked examples and a lot of detailed explanation.

Second, try to use the [modelsummary] tag on StackOverflow.

Third, if you think you found a bug or have a feature request, please file it on the Github issue tracker:

How can I add or modify statistics in a table?

See the detailed documentation in the “Adding and Customizing Models” section of the modelsummary website.

How does `modelsummary` extract estimates and goodness-of-fit statistics?

A modelsummary table is divided in two parts: “Estimates” (top of the table) and “Goodness-of-fit” (bottom of the table). To populate those two parts, modelsummary tries using the broom, parameters and performance packages in sequence.

Estimates:

Try the broom::tidy function to see if that package supports this model type, or if the user defined a custom tidy function in their global environment. If this fails…
Try the parameters::model_parameters function to see if the parameters package supports this model type.

Goodness-of-fit:

Try the performance::model_performance function to see if the performance package supports this model type.
Try the broom::glance function to see if that package supports this model type, or if the user defined a custom glance function in their global environment. If this fails…

You can change the order in which those steps are executed by setting a global option:

# tidymodels: broom 
options(modelsummary_get = "broom")

# easystats: performance + parameters
options(modelsummary_get = "easystats")

If all of this fails, modelsummary will return an error message.

If you have problems with a model object, you can often diagnose the problem by running the following commands from a clean R session:

# see if parameters and performance support your model type
library(parameters)
library(performance)
model_parameters(model)
model_performance(model)

# see if broom supports your model type
library(broom)
tidy(model)
glance(model)

# see if broom.mixed supports your model type
library(broom.mixed)
tidy(model)
glance(model)

If none of these options work, you can create your own tidy and glance methods, as described in the Adding new models section.

If one of the extractor functions does not work well or takes too long to process, you can define a new “custom” model class and choose your own extractors, as described in the Adding new models section.

How can I speed up `modelsummary`?

The modelsummary function, by itself, is not slow: it should only take a couple seconds to produce a table in any output format. However, sometimes it can be computationally expensive (and long) to extract estimates and to compute goodness-of-fit statistics for your model.

The main options to speed up modelsummary are:

Set gof_map=NA to avoid computing expensive goodness-of-fit statistics.
Use the easystats extractor functions and the metrics argument to avoid computing expensive statistics (see below for an example).
Use parallel computation if you are summarizing multiple models. See the “Parallel computation” section in the ?modelsummary documentation.

To diagnose the slowdown and find the bottleneck, you can try to benchmark the various extractor functions:

library(tictoc)

data(trade)
mod <- lm(mpg ~ hp + drat, mtcars)

tic("tidy")
x <- broom::tidy(mod)
toc()
#> tidy: 0.003 sec elapsed

tic("glance")
x <- broom::glance(mod)
toc()
#> glance: 0.005 sec elapsed

tic("parameters")
x <- parameters::parameters(mod)
toc()
#> parameters: 0.027 sec elapsed

tic("performance")
x <- performance::performance(mod)
toc()
#> performance: 0.014 sec elapsed

In my experience, the main bottleneck tends to be computing goodness-of-fit statistics. The performance extractor allows users to specify a metrics argument to select a subset of GOF to include. Using this can speedup things considerably.

We call modelsummary with the metrics argument:

modelsummary(mod, metrics = "rmse")

	(1)
(Intercept)	10.790
	(5.078)
hp	-0.052
	(0.009)
drat	4.698
	(1.192)
Num.Obs.	32
R2	0.741
R2 Adj.	0.723
AIC	169.5
BIC	175.4
Log.Lik.	-80.752
F	41.522

Escaped LaTeX characters

Sometimes, users want to include raw LaTeX commands in their tables, such as coefficient names including math mode: Apple $\times$ Orange. The result of these attempts is often a weird string such as: \$\textbackslash{}times\$ instead of proper LaTeX-rendered characters.

The source of the problem is that kableExtra, default table-making package in modelsummary, automatically escapes weird characters to make sure that your tables compile properly in LaTeX. To avoid this, we need to pass the escape=FALSE to modelsummary:

modelsummary(mod, escape = FALSE)

Bayesian models

Many bayesian models are supported out-of-the-box, including those produced by the rstanarm and brms packages. The statistics available for bayesian models are slightly different than those available for most frequentist models. Users can call get_estimates to see what is available:

library(rstanarm)
#> This is rstanarm version 2.26.1
#> - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
#> - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
#> - For execution on a local, multicore CPU with excess RAM we recommend calling
#>   options(mc.cores = parallel::detectCores())
#> 
#> Attaching package: 'rstanarm'
#> The following object is masked from 'package:fixest':
#> 
#>     se
mod <- stan_glm(am ~ hp + drat, data = mtcars)

get_estimates(mod)
#>          term      estimate         mad conf.level     conf.low    conf.high
#> 1 (Intercept) -2.2431935597 0.582889975       0.95 -3.460763504 -1.093516419
#> 2          hp  0.0007522099 0.001042313       0.95 -0.001426489  0.002933257
#> 3        drat  0.7082772964 0.136557529       0.95  0.434794815  1.000252606
#>   prior.distribution prior.location prior.scale group std.error statistic
#> 1             normal        0.40625  1.24747729              NA        NA
#> 2             normal        0.00000  0.01819465              NA        NA
#> 3             normal        0.00000  2.33313429              NA        NA
#>   p.value
#> 1      NA
#> 2      NA
#> 3      NA

This shows that there is no std.error column, but that there is a mad statistic (mean absolute deviation). So we can do:

modelsummary(mod, statistic = "mad")
#> Warning: 
#> `modelsummary` uses the `performance` package to extract goodness-of-fit
#> statistics from models of this class. You can specify the statistics you wish
#> to compute by supplying a `metrics` argument to `modelsummary`, which will then
#> push it forward to `performance`. Acceptable values are: "all", "common",
#> "none", or a character vector of metrics names. For example: `modelsummary(mod,
#> metrics = c("RMSE", "R2")` Note that some metrics are computationally
#> expensive. See `?performance::performance` for details.
#>  This warning appears once per session.

	(1)
(Intercept)	-2.243
	(0.583)
hp	0.001
	(0.001)
drat	0.708
	(0.137)
Num.Obs.	32
R2	0.502
R2 Adj.	0.431
Log.Lik.	-12.058
ELPD	-15.1
ELPD s.e.	3.1
LOOIC	30.2
LOOIC s.e.	6.2
WAIC	29.9
RMSE	0.34

As noted in the modelsummary() documentation, model results are extracted using the parameters package. Users can pass additional arguments to modelsummary(), which will then push forward those arguments to the parameters::parameters function to change the results. For example, the parameters documentation for bayesian models shows that there is a centrality argument, which allows users to report the mean and standard deviation of the posterior distribution, instead of the median and MAD:

get_estimates(mod, centrality = "mean")
#>          term      estimate     std.dev conf.level     conf.low    conf.high
#> 1 (Intercept) -2.2641488828 0.598578296       0.95 -3.460763504 -1.093516419
#> 2          hp  0.0007461579 0.001088488       0.95 -0.001426489  0.002933257
#> 3        drat  0.7118226842 0.141512693       0.95  0.434794815  1.000252606
#>   prior.distribution prior.location prior.scale group std.error statistic
#> 1             normal        0.40625  1.24747729              NA        NA
#> 2             normal        0.00000  0.01819465              NA        NA
#> 3             normal        0.00000  2.33313429              NA        NA
#>   p.value
#> 1      NA
#> 2      NA
#> 3      NA

modelsummary(mod, statistic = "std.dev", centrality = "mean")

	(1)
(Intercept)	-2.264
	(0.599)
hp	0.001
	(0.001)
drat	0.712
	(0.142)
Num.Obs.	32
R2	0.502
R2 Adj.	0.431
Log.Lik.	-12.058
ELPD	-15.1
ELPD s.e.	3.1
LOOIC	30.2
LOOIC s.e.	6.2
WAIC	29.9
RMSE	0.34

We can also get additional test statistics using the test argument:

get_estimates(mod, test = c("pd", "rope"))
#>          term      estimate         mad conf.level     conf.low    conf.high
#> 1 (Intercept) -2.2431935597 0.582889975       0.95 -3.460763504 -1.093516419
#> 2          hp  0.0007522099 0.001042313       0.95 -0.001426489  0.002933257
#> 3        drat  0.7082772964 0.136557529       0.95  0.434794815  1.000252606
#>        pd rope.percentage prior.distribution prior.location prior.scale group
#> 1 0.99975               0             normal        0.40625  1.24747729      
#> 2 0.76125               1             normal        0.00000  0.01819465      
#> 3 1.00000               0             normal        0.00000  2.33313429      
#>   std.error statistic p.value
#> 1        NA        NA      NA
#> 2        NA        NA      NA
#> 3        NA        NA      NA

output: print and save

fmt: round and format

estimate

statistic: SE, t, p, CI, etc.

vcov: robust SEs

stars

coef_omit

coef_rename

coef_map: order, omit, rename

gof_omit: goodness-of-fit and model characteristics