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'

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
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

## modelsummary function arguments

### output

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

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

• Integer: Number of digits to keep after the period, including trailing zeros.
• String: Formatting string passed to the sprintf function. See ?sprintf
• Function: Returns a character string in the custom format you want.

For example:

modelsummary(models, fmt = 4)

# 4 digits and trailing zero
modelsummary(models, fmt = "%.4f")

# scientific notation
modelsummary(models, fmt = "%.4e")

# custom function with big mark commas
modelsummary(models, fmt = function(x) format(round(x, 0), big.mark=","))

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
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

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
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

### statistic

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
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

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 (0.000) 0.006 (0.000) 2611.140 (0.000) 0.003 (0.000) 1011.240 (0.000)
Literacy −39.121 0.003 3.680 0.000 −68.507
37.052 (0.294) 0.000 (0.000) 46.552 (0.937) 0.000 (0.000) 18.029 (0.000)
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 (0.000) 0.000 (0.000)
Num.Obs. 86 86 86 86 86
R2 0.020 0.065 0.152
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

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 = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000
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 = 0.000 p = 0.937 p = 0.000 p = 0.000
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 = 0.000 p = 0.000

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

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:

1. NULL omits any stars or special marks (default)
2. TRUE uses these default values: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
3. 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: rename/omit/map

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

#### coef_rename

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"))
Model 1 Model 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
AIC 359.2 342.7
BIC 363.6 347.1
Log.Lik. −176.588 −168.347
F 7.565 32.876

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
AIC 167.4 327.2
BIC 176.2 336.0
Log.Lik. −77.719 −157.623
F 24.774 20.903

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)
Model 1 Model 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
AIC 338.4 342.8
BIC 344.3 348.7
Log.Lik. −165.218 −167.399
F 22.454 17.743

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.

#### coef_map

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
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

#### coef_omit

An alternative mechanism to subset coefficients is to use the coef_omit argument. This string is a regular expression which will be fed to stringr::str_detect to detect the variable names which should be excluded from the table.

modelsummary(models, coef_omit = "Intercept|Commerce", gof_omit=".*")
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 the [^] construct that specifies what not to match, we use the | pipe operator to separate the list of variables to include:

modelsummary(models, coef_omit = "[^Commerce|(Intercept)]", 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)
Commerce 0.011 0.001
(0.000) (0.000)

Another way to achieve the same result would be to use fancy look-ahead or look-behind assertions. For example, if you want the opposite of coef_omit (call it coef_keep), and you want to “keep” parameters that match a certain pattern, you could try:

modelsummary(models, coef_omit = "^(?!.*Commerce)", gof_omit=".*")
OLS 1 Poisson 1 OLS 2 Poisson 2 OLS 3
Commerce 0.011 0.001
(0.000) (0.000)

You can chain those look-behinds as needed to match multiple patterns (noting that parentheses must be escaped):

modelsummary(models, coef_omit = "^(?!.*Commerce)(?!.*\Int)", 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) Commerce 0.011 0.001 (0.000) (0.000) ### gof_omit gof_omit is a regular expression which will be fed to stringr::str_detect 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 A more powerful mechanism is to supply a data.frame (or tibble) through the gof_map argument. This data.frame must include 3 columns: 1. raw: a string with the name of a column produced by broom::glance(model). 2. clean: a string with the “clean” name of the statistic you want to appear in your final table. 3. 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 gmomit <- 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, which is passed through the ellipsis (...) to the kableExtra table-making package: 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 R2 0.02 0.07 0.15 ### group Some models produce “grouped” parameter estimates. For example, multinomial logit models produce estimates for each level of the outcome variable, and GAMLSS models produce estimates for different “components.” To display the results of such models, we can specify the kind of table we want by feeding a two sided formula to the group argument. The left-hand side of the formula governs rows, and the right-hand side governs columns. The order of terms determines the order of columns in the final table. The formula must have 3 terms: “model”, “term”, and the name of the group identifier. The 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 “y.level”: library(nnet) dat_multinom <- mtcars dat_multinomcyl <- sprintf("Cyl: %s", dat_multinomcyl) 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]]) #> # A tibble: 4 × 8 #> y.level term estimate std.error statistic p.value conf.low conf.high #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Cyl: 6 (Intercept) 47.3 35.0 1.35 0.177 -21.3 116. #> 2 Cyl: 6 mpg -2.21 1.64 -1.35 0.178 -5.42 1.00 #> 3 Cyl: 8 (Intercept) 72.4 37.2 1.95 0.0513 -0.422 145. #> 4 Cyl: 8 mpg -3.58 1.77 -2.02 0.0437 -7.06 -0.102 To summarize the results, we can type: modelsummary(mod, group = y.level + term ~ model) Model 1 Model 2 Cyl: 6 (Intercept) 47.252 89.573 (34.975) (86.884) mpg −2.205 −3.627 (1.638) (3.869) drat −3.210 (3.810) Cyl: 8 (Intercept) 72.440 117.971 (37.175) (87.998) mpg −3.580 −4.838 (1.775) (3.915) drat −5.028 (4.199) Num.Obs. 32 32 AIC 24.1 24.5 edf 4.000 6.000  modelsummary(mod, group = model + term ~ y.level) Cyl: 6 Cyl: 8 Model 1 (Intercept) 47.252 72.440 (34.975) (37.175) mpg −2.205 −3.580 (1.638) (1.775) Model 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)  modelsummary(mod, group = term ~ model + y.level) Model 1 / Cyl: 6 Model 1 / Cyl: 8 Model 2 / Cyl: 6 Model 2 / Cyl: 8 (Intercept) 47.252 72.440 89.573 117.971 (34.975) (37.175) (86.884) (87.998) mpg −2.205 −3.580 −3.627 −4.838 (1.638) (1.775) (3.869) (3.915) drat −3.210 −5.028 (3.810) (4.199) Sometimes, we want to combine models with and without grouped parameters. For instance, we may want to compare the results from a GAMLSS model to the results from a GLM model. In GAMLSS models, the group id is called parameter, so we call: library(gamlss) dat <- rgamma(100, shape=1, scale=10) mod <- list( "GA" = gamlss(dat ~ 1, family = GA, trace = FALSE), "GAGLM" = glm(dat ~ 1, family = Gamma("log"))) modelsummary(mod, group = parameter + term ~ model) #> ****************************************************************** #> Family: c("GA", "Gamma") #> #> Call: gamlss(formula = dat ~ 1, family = GA, trace = FALSE) #> #> Fitting method: RS() #> #> ------------------------------------------------------------------ #> Mu link function: log #> Mu Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 2.27587 0.09159 24.85 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> ------------------------------------------------------------------ #> Sigma link function: log #> Sigma Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) -0.08786 0.06321 -1.39 0.168 #> #> ------------------------------------------------------------------ #> No. of observations in the fit: 100 #> Degrees of Freedom for the fit: 2 #> Residual Deg. of Freedom: 98 #> at cycle: 2 #> #> Global Deviance: 653.3311 #> AIC: 657.3311 #> SBC: 662.5414 #> ****************************************************************** GA GAGLM (Intercept) −0.088 2.276 group 2.276 2.276 (0.063) (0.090) (0.092) (0.090) Num.Obs. 100 100 AIC 657.3 658.3 BIC 662.5 663.5 Log.Lik. −326.666 −327.131 This table is “OK”, but the GLM intercept corresponds to the GAMLSS “mu” intercept, so we may want to align those quantities. The problem is that modelsummary doesn’t have the same statistical knowledge we do: Applying the get_estimates function to the GLM model does not produce a parameter column. From the modelsummary developpers’ perspective, it is impossible to know in advance how users will want to align grouped and non-grouped coefficients. (What if a user tries to combine a GLM with a multinomial logit?!) As a result, some additional user intervention is necessary to clearly indicate which rows need to be aligned. To do this, we create a modelsummary_list object for our GLM model, and we add a new parameter column to its tidy component (See section on “Adding New Models” below). mod_ga <- gamlss(dat ~ 1, trace = FALSE) mod_glm <- glm(dat ~ 1, family = Gamma("log")) # convert glm to modelsummary_list object mod_glm <- modelsummary(mod_glm, output = "modelsummary_list") # add a group id mod_glmtidyparameter <- "mu" # summarize the table modelsummary(list(mod_ga, mod_glm), group = parameter + term ~ model) #> ****************************************************************** #> Family: c("NO", "Normal") #> #> Call: gamlss(formula = dat ~ 1, trace = FALSE) #> #> Fitting method: RS() #> #> ------------------------------------------------------------------ #> Mu link function: identity #> Mu Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 9.7364 0.8754 11.12 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> ------------------------------------------------------------------ #> Sigma link function: log #> Sigma Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 2.16947 0.07071 30.68 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> ------------------------------------------------------------------ #> No. of observations in the fit: 100 #> Degrees of Freedom for the fit: 2 #> Residual Deg. of Freedom: 98 #> at cycle: 2 #> #> Global Deviance: 717.6821 #> AIC: 721.6821 #> SBC: 726.8924 #> ****************************************************************** Model 1 Model 2 (Intercept) 2.169 9.736 (0.071) (0.875) mu 2.276 (0.090) Num.Obs. 100 100 AIC 721.7 658.3 BIC 726.9 663.5 Log.Lik. −358.841 −327.131 ### align 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 Add notes to the bottom of your table: modelsummary(models, notes = list('Text of the first note.', 'Text of the second note.')) ### title 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 ## Exponentiated coefficients (and other extras) Users can pass any additional argument they want to the tidy method which is used to extract estimates from a model. For example, in logistic or Cox proportional hazard models, many users want to exponentiate coefficients to facilitate interpretation. The tidy functions supplied by the broom package allow users to set exponentiate=TRUE to achieve this. In modelsummary, users can use the same argument: mod_logit <- glm(am ~ mpg, data = mtcars, family = binomial) modelsummary(mod_logit, exponentiate = TRUE) Any argument supported by tidy is thus supported by modelsummary. An important technical point is that broom::tidy still reports standard errors on the original scale, and not on the scale of the exponentiated coefficients. This is because, as discussed on the broom issue tracker, “quantiles (and thereby confidence intervals) are invariant under monotonic transformation (exp()) but variances are not.” The first alternative strategy is thus to report confidence intervals instead of standard errors. When exponentiate=TRUE, estimates and intervals will be displayed on the same scale: modelsummary(mod, statistic = "conf.int", exponentiate = TRUE) The second alternative strategy si to use a different “backend” to extract information from model objects. By setting the modelsummary_get global option, we tell modelsummary to use the easystats/parameters packages instead of broom. With these packages, the standard errors will automatically be transformed using a linear approximation of the transformation (e.g., \(e^\beta * SE for logit models):

options("modelsummary_get" = "easystats")
modelsummary(mod, exponentiate = TRUE)

Finally, we can define a glance_custom.glm function to transform the results of all GLM models automatically, using the same linear approximation. This strategy is described in detail in the section on “Customizing existing models”, so we just show working code here without further explanation:

tidy_custom.glm <- function(x) {
out <- broom::tidy(x)
out$std.error <- exp(out$estimate) * out$std.error out$estimate <- exp(out$estimate) return(out) } modelsummary(mod) ## Custom appearance To customize the appearance of tables, modelsummary supports four 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] "confint.glht" "confusionMatrix" #> [45] "coxph" "cpglmm" #> [47] "crr" "cv.glmnet" #> [49] "data.frame" "dbscan" #> [51] "default" "density" #> [53] "dep.effect" "dgCMatrix" #> [55] "dgTMatrix" "DirichletRegModel" #> [57] "dist" "drc" #> [59] "durbinWatsonTest" "emm_list" #> [61] "emmeans" "emmeans_summary" #> [63] "emmGrid" "epi.2by2" #> [65] "ergm" "fa" #> [67] "fa.ci" "factanal" #> [69] "FAMD" "felm" #> [71] "fitdistr" "fixest" #> [73] "ftable" "gam" #> [75] "Gam" "gamlss" #> [77] "gamm" "garch" #> [79] "geeglm" "ggeffects" #> [81] "glht" "glimML" #> [83] "glm" "glmm" #> [85] "glmmTMB" "glmnet" #> [87] "glmrob" "glmRob" #> [89] "glmx" "gmm" #> [91] "hclust" "hdbscan" #> [93] "hkmeans" "HLfit" #> [95] "htest" "hurdle" #> [97] "irlba" "ivFixed" #> [99] "ivprobit" "ivreg" #> [101] "kappa" "kde" #> [103] "Kendall" "kmeans" #> [105] "lavaan" "leveneTest" #> [107] "Line" "Lines" #> [109] "list" "lm" #> [111] "lm.beta" "lme" #> [113] "lmodel2" "lmrob" #> [115] "lmRob" "logical" #> [117] "logistf" "logitmfx" #> [119] "logitor" "lqm" #> [121] "lqmm" "lsmobj" #> [123] "manova" "maov" #> [125] "map" "margins" #> [127] "maxim" "maxLik" #> [129] "Mclust" "mcmc" #> [131] "mcmc.list" "MCMCglmm" #> [133] "mcp1" "mcp2" #> [135] "med1way" "mediate" #> [137] "merMod" "merModList" #> [139] "meta_bma" "meta_fixed" #> [141] "meta_random" "metaplus" #> [143] "mfx" "mhurdle" #> [145] "mipo" "mira" #> [147] "mixed" "MixMod" #> [149] "mixor" "mjoint" #> [151] "mle" "mle2" #> [153] "mlm" "mlogit" #> [155] "model_fit" "model_parameters" #> [157] "muhaz" "multinom" #> [159] "mvord" "negbin" #> [161] "negbinirr" "negbinmfx" #> [163] "nlrq" "nls" #> [165] "NULL" "numeric" #> [167] "omega" "onesampb" #> [169] "optim" "orcutt" #> [171] "osrt" "pairwise.htest" #> [173] "pam" "parameters_efa" #> [175] "parameters_pca" "PCA" #> [177] "pgmm" "plm" #> [179] "PMCMR" "poissonirr" #> [181] "poissonmfx" "poLCA" #> [183] "polr" "Polygon" #> [185] "Polygons" "power.htest" #> [187] "prcomp" "principal" #> [189] "probitmfx" "pvclust" #> [191] "pyears" "rcorr" #> [193] "ref.grid" "regsubsets" #> [195] "ridgelm" "rlm" #> [197] "rlmerMod" "rma" #> [199] "robtab" "roc" #> [201] "rq" "rqs" #> [203] "rqss" "sarlm" #> [205] "Sarlm" "scam" #> [207] "selection" "sem" #> [209] "SemiParBIV" "sparseMatrix" #> [211] "SpatialLinesDataFrame" "SpatialPolygons" #> [213] "SpatialPolygonsDataFrame" "spec" #> [215] "speedglm" "speedlm" #> [217] "stanfit" "stanmvreg" #> [219] "stanreg" "summary_emm" #> [221] "summary.glht" "summary.lm" #> [223] "summary.plm" "summaryDefault" #> [225] "survdiff" "survexp" #> [227] "survfit" "survreg" #> [229] "svd" "svyglm" #> [231] "svyolr" "systemfit" #> [233] "t1way" "table" #> [235] "tobit" "trendPMCMR" #> [237] "trimcibt" "ts" #> [239] "TukeyHSD" "varest" #> [241] "vgam" "wbgee" #> [243] "wbm" "wmcpAKP" #> [245] "xyz" "yuen" #> [247] "zcpglm" "zerocount" #> [249] "zeroinfl" "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 ## 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) Model 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') Two 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, 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) #> #> Re-fitting to get Hessian #> # A tibble: 4 × 7 #> term estimate std.error statistic conf.low conf.high coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 mpg -0.00865 0.0903 -0.0957 -0.192 0.171 coefficient #> 2 drat 3.95 1.31 3.02 1.62 6.85 coefficient #> 3 3|4 14.0 4.04 3.46 NA NA scale #> 4 4|5 16.9 4.39 3.85 NA NA scale 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) Model 1 mpg −0.009 (0.090) drat 3.949** (1.307) 3|4 13.963 (4.041) 4|5 16.899 (4.395) Num.Obs. 32 AIC 51.1 BIC 57.0 Log.Lik. −21.547 edf 4.000 + 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)
Model 1 Model 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
AIC 337.9 46.4
BIC 343.7 53.7
Log.Lik. −164.940 −18.201
F 23.100 38.066
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) <- c("custom", class(mod_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(x)
)
}

# summarize
modelsummary(mod_custom)
Model 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 (...).

## 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$tidy #> # A tibble: 3 × 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 279. 55.4 5.03 0.0000236 #> 2 mpg -9.99 1.79 -5.57 0.00000517 #> 3 drat 19.1 20.2 0.947 0.352 mod_list$glance
#> # A tibble: 1 × 13
#>   r.squared adj.r.squared sigma statistic     p.value    df logLik   AIC   BIC
#>       <dbl>         <dbl> <dbl>     <dbl>       <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1     0.614         0.588  44.0      23.1 0.000000999     2  -165.  338.  344.
#> # … with 4 more variables: deviance <dbl>, df.residual <int>, nobs <int>,
#> #   F <dbl>

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")
#> 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: modelsummary(mod,metrics=c("RMSE","R2") See ?performance::performance for more information. Please note that some statistics are expensive to compute.
#> This warning is displayed once per session.
# 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 (0.000) *** 1.736 (0.086) + Num.Obs. 32 32 RMSE 4.28 4.28 ## 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: This .Rmd file shows illustrates how to use table numbering and cross-references to produce PDF documents using bookdown: This .Rmd file shows how to customize tables in PDF and HTML files using gt and kableExtra functions: ## 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 F 3.398 0.082 1.050 ### 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 = ".*") %>% 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) Model 1 Model 2 (Intercept) 328.546 280.750 (29.925) (42.564) mpg −9.065 −10.227 (1.324) (2.458) drat 19.573 (21.660) 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 ### 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: 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$x_inst_1 + rnorm(150, sd = 0.5) # estimate an instrumental variable model mod <- feols(y ~ x1 | fe | x_endo_1 + x_endo_2 ~ x_inst_1 + x_inst_2, base) # custom extractor function returns a one-row data.frame (or tibble) glance_custom.fixest <- function(x) { tibble("IV Wald" = fitstat(x, "ivwald")[[1]]$stat)
}

# draw table
modelsummary(mod)
Model 1
fit_x_endo_1 0.987
(0.653)
fit_x_endo_2 3.168
(3.050)
x1 0.366
(0.863)
Num.Obs. 150
R2 −2.734
R2 Within −8.794
R2 Pseudo
AIC 577.7
BIC 595.8
Log.Lik. −282.855
Std.Errors by: fe
FE: fe X
IV Wald 3.135

### 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) 83.364 90.259 90.222
(16.769) (14.822) (14.752)
Literacy −0.567 −0.703 −0.661
(0.268) (0.236) (0.227)
Commerce −0.467 −0.506 −0.506
(0.182) (0.155) (0.159)
Num.Obs. 57 86 86
Num.Imp. 5 5
R2 0.112 0.136 0.138
AIC 532.0
BIC 540.2
Log.Lik. −261.993
F 3.396

## Backends

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:

• jpg
• png

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.

### 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:

1. 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…
2. Try the parameters::model_parameters function to see if the parameters package supports this model type.

Goodness-of-fit:

1. 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…
2. Try the performance::model_performance function to see if the performance package supports this model type.

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 broom supports your model type
library(broom)
tidy(model)
glance(model)

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

### Why is modelsummary so slow?

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 models.

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

library(tictoc)

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

tic("tidy")
x <- broom::tidy(mod)
toc()
#> tidy: 0.007 sec elapsed

tic("glance")
x <- broom::glance(mod)
toc()
#> glance: 0.006 sec elapsed

tic("parameters")
x <- parameters::parameters(mod)
toc()
#> parameters: 0.094 sec elapsed

tic("performance")
x <- performance::performance(mod)
toc()
#> performance: 0.033 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.

First, we set a global option to run the performance extractor first, before broom::glance:

options(modelsummary_get = "easystats")

Then, we call modelsummary with the metrics argument:

modelsummary(mod, metrics = "rmse")
Model 1
(Intercept) 10.790
(5.078)
hp −0.052
(0.009)
drat 4.698
(1.192)
Num.Obs. 32
F 41.522
RMSE 3.02

Going back to the original setting:

options(modelsummary_get = "broom")

### 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 option to kableExtra. The modelsummary can push this option forward to kableExtra through the ... ellipsis. This means that users can often just call something like:

modelsummary(mod, escape = FALSE)