Create a regression table with multiple panels — panelsummary • panelsummary

`panelsummary` Creates a beautiful and customizable regression table with panels. This function is best used to summarize multiple dependent variables that are passed through the same regression models. This function returns a kableExtra object which can then be edited using kableExtra's suite of functions.

Usage

panelsummary(
  ...,
  panel_labels = NULL,
  mean_dependent = FALSE,
  colnames = NULL,
  caption = NULL,
  format = NULL,
  pretty_num = FALSE,
  collapse_fe = FALSE,
  bold = FALSE,
  italic = FALSE,
  hline_after = FALSE,
  hline_before_fe = TRUE,
  fmt = 3,
  estimate = "estimate",
  statistic = "std.error",
  vcov = NULL,
  conf_level = 0.95,
  stars = FALSE,
  coef_map = NULL,
  coef_omit = NULL,
  coef_rename = NULL,
  gof_map = NULL,
  gof_omit = NULL
)

Arguments

...

A regression model or models (see panelsummary::models_supported for classes that are supported). * The regression model can be a list of models or a singular object. * If a list is passed in, one column for each list is created. Each argument will correspond to a panel. * If only one object is passed in, there will be no panels and the output will be similar to evaluating modelsummary::modelsummary() followed by kableExtra::kbl()

panel_labels

A character vector. How to label each panel in the table. * `NULL` (the default): the panels will be labeled "Panel A:", "Panel B:",...etc.

mean_dependent

A boolean. For use with fixest objects only. * `FALSE` (the default): the mean of the dependent variable will not be shown in the resulting table. * `TRUE`: the mean of the dependent variable will be shown in the resulting table.

colnames

An optional vector of strings. The vector of strings should have the same length as the number columns of the table. * `NULL` (the default): colnames are defaulted to a whitespace, followed by (1), (2), ....etc.

caption

A string. The table caption.

format

A character string. Possible values are latex, html, pipe (Pandoc's pipe tables), simple (Pandoc's simple tables), and rst. The value of this argument will be automatically determined if the function is called within a knitr document. The format value can also be set in the global option knitr.table.format. If format is a function, it must return a character string.

pretty_num

A logical. If TRUE, then numbers over 999 have a comma printing format.

collapse_fe

A boolean. For use with fixest objects only. Determines whether fixed effects should only be included in the bottom of the table. This is suited for when each panel has the same models with the same fixed effects. * `FALSE` (the default): fixed effects are shown in each panel. * `TRUE`: fixed effects are shown only at the bottom of the final panel, separated by a horizontal line (see hline_before_fe)

bold

A boolean. Determines whether the panel names should be in bold font. * `FALSE` (the default): the panel names are not in bold. * `TRUE`: the panel names are bolded

italic

A boolean. Determines whether the panel names should be in italics. * `FALSE` (the default): the panel names are not in italics. * `TRUE`: the panel names will be in italics.

hline_after

A boolean. Adds a horizontal line after the panel labels. * `FALSE` (the default): there is not horizonal line after the panel labels. * `TRUE`: a horizontal line will appear after the panel labels.

hline_before_fe

A boolean. To be used only when collapse_fe = TRUE, and hence with fixest objects only. Adds a horizontal line before the fixed effects portion of the table.

fmt

how to format numeric values: integer, user-supplied function, or modelsummary function.

Integer: Number of decimal digits
User-supplied functions:
- Any function which accepts a numeric vector and returns a character vector of the same length.
modelsummary functions:
- fmt = fmt_significant(2): Two significant digits (at the term-level)
- fmt = fmt_decimal(digits = 2, pdigits = 3): Decimal digits for estimate and p values
- fmt = fmt_sprintf("%.3f"): See ?sprintf
- fmt = fmt_term("(Intercept)" = 1, "X" = 2): Format terms differently
- fmt = fmt_statistic("estimate" = 1, "r.sqared" = 6): Format statistics differently.
- fmt = fmt_identity(): unformatted raw values
string:
Note on LaTeX output: To ensure proper typography, all numeric entries are enclosed in the \num{} command, which requires the siunitx package to be loaded in the LaTeX preamble. This behavior can be altered with global options. See the 'Details' section.

estimate

a single string or a character vector of length equal to the number of models. Valid entries include any column name of the data.frame produced by get_estimates(model), and strings with curly braces compatible with the glue package format. Examples:

"estimate"
"{estimate} ({std.error}){stars}"
"{estimate} [{conf.low}, {conf.high}]"

statistic

vector of strings or glue strings which select uncertainty statistics to report vertically below the estimate. NULL omits all uncertainty statistics.

"conf.int", "std.error", "statistic", "p.value", "conf.low", "conf.high", . or any column name produced by get_estimates(model)
glue package strings with braces, with or without R functions, such as:
- "{p.value} [{conf.low}, {conf.high}]"
- "Std.Error: {std.error}"
- `"exp(estimate) * std.error"
Numbers are automatically rounded and converted to strings. To apply functions to their numeric values, as in the last glue example, users must set fmt=NULL.
Parentheses are added automatically unless the string includes glue curly braces {}.
Some statistics are not supported for all models. See column names in get_estimates(model), and visit the website to learn how to add custom statistics.

vcov

robust standard errors and other manual statistics. The vcov argument accepts six types of input (see the 'Details' and 'Examples' sections below):

NULL returns the default uncertainty estimates of the model object
string, vector, or (named) list of strings. "iid", "classical", and "constant" are aliases for NULL, which returns the model's default uncertainty estimates. The strings "HC", "HC0", "HC1" (alias: "stata"), "HC2", "HC3" (alias: "robust"), "HC4", "HC4m", "HC5", "HAC", "NeweyWest", "Andrews", "panel-corrected", "outer-product", and "weave" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (...), but it is safer to define your own custom functions as described in the next bullet.
function or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster="country")).
formula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).
named list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.
a named list of length(models) vectors with names equal to the names of your coefficient estimates. See 'Examples' section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.

conf_level

numeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.

stars

to indicate statistical significance

FALSE (default): no significance stars.
TRUE: +=.1, *=.05, **=.01, ***=0.001
Named numeric vector for custom stars such as c('*' = .1, '+' = .05)
Note: a legend will not be inserted at the bottom of the table when the estimate or statistic arguments use "glue strings" with {stars}.

coef_map

character vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c("hp:mpg"="HPxM/G"). See Examples section below.

coef_omit

integer vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible "negative lookaheads" can be used to specify which coefficients to keep in the table. Examples:

c(2, 3, 5): omits the second, third, and fifth coefficients.
c(-2, -3, -5): negative values keep the second, third, and fifth coefficients.
"ei": omit coefficients matching the "ei" substring.
"^Volume$": omit the "Volume" coefficient.
"ei|rc": omit coefficients matching either the "ei" or the "rc" substrings.
"^(?!Vol)": keep coefficients starting with "Vol" (inverse match using a negative lookahead).
"^(?!.*ei)": keep coefficients matching the "ei" substring.
"^(?!.*ei|.*pt)": keep coefficients matching either the "ei" or the "pt" substrings.
See the Examples section below for complete code.

coef_rename

logical, named or unnamed character vector, or function

Logical: TRUE renames variables based on the "label" attribute of each column. See the Example section below.
Unnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.
Named character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c("hp:mpg"="hp X mpg")
Function: Accepts a character vector of the model's term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)

gof_map

rename, reorder, and omit goodness-of-fit statistics and other model information. This argument accepts 4 types of values:

NULL (default): the modelsummary::gof_map dictionary is used for formatting, and all unknown statistic are included.
character vector: "all", "none", or a vector of statistics such as c("rmse", "nobs", "r.squared"). Elements correspond to colnames in the data.frame produced by get_gof(model). The modelsummary::gof_map default dictionary is used to format and rename statistics.
NA: excludes all statistics from the bottom part of the table.
data.frame with 3 columns named "raw", "clean", "fmt". Unknown statistics are omitted. See the 'Examples' section below.
list of lists, each of which includes 3 elements named "raw", "clean", "fmt". Unknown statistics are omitted. See the 'Examples section below'.

gof_omit

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

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

Value

A kableExtra object that is instantly customizable by kableExtra's suite of functions.

Examples


# Panelsummary with lm -------------------------

reg_1 <- lm(mpg ~ hp + cyl, data = mtcars)
reg_2 <- lm(disp ~ hp + cyl, data = mtcars)

panelsummary(reg_1, reg_2, panel_labels = c("Panel A: MPG", "Panel B: Displacement"))
#> <table>
#>  <thead>
#>   <tr>
#>    <th style="text-align:left;">   </th>
#>    <th style="text-align:center;"> (1) </th>
#>   </tr>
#>  </thead>
#> <tbody>
#>   <tr grouplength="13"><td colspan="2" style="border-bottom: 1px solid;">Panel A: MPG</td></tr>
#> <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> (Intercept) </td>
#>    <td style="text-align:center;"> 36.908 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1">  </td>
#>    <td style="text-align:center;"> (2.191) </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> hp </td>
#>    <td style="text-align:center;"> -0.019 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1">  </td>
#>    <td style="text-align:center;"> (0.015) </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> cyl </td>
#>    <td style="text-align:center;"> -2.265 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1">  </td>
#>    <td style="text-align:center;"> (0.576) </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> Num.Obs. </td>
#>    <td style="text-align:center;"> 32 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> R2 </td>
#>    <td style="text-align:center;"> 0.741 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> R2 Adj. </td>
#>    <td style="text-align:center;"> 0.723 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> AIC </td>
#>    <td style="text-align:center;"> 169.6 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> BIC </td>
#>    <td style="text-align:center;"> 175.4 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> Log.Lik. </td>
#>    <td style="text-align:center;"> -80.781 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> RMSE </td>
#>    <td style="text-align:center;"> 3.02 </td>
#>   </tr>
#>   <tr grouplength="13"><td colspan="2" style="border-bottom: 1px solid;">Panel B: Displacement</td></tr>
#> <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> (Intercept) </td>
#>    <td style="text-align:center;"> -144.569 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1">  </td>
#>    <td style="text-align:center;"> (37.652) </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> hp </td>
#>    <td style="text-align:center;"> 0.236 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1">  </td>
#>    <td style="text-align:center;"> (0.258) </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> cyl </td>
#>    <td style="text-align:center;"> 55.063 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1">  </td>
#>    <td style="text-align:center;"> (9.898) </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> Num.Obs. </td>
#>    <td style="text-align:center;"> 32 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> R2 </td>
#>    <td style="text-align:center;"> 0.819 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> R2 Adj. </td>
#>    <td style="text-align:center;"> 0.806 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> AIC </td>
#>    <td style="text-align:center;"> 351.6 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> BIC </td>
#>    <td style="text-align:center;"> 357.4 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> Log.Lik. </td>
#>    <td style="text-align:center;"> -171.793 </td>
#>   </tr>
#>   <tr>
#>    <td style="text-align:left;padding-left: 2em;" indentlevel="1"> RMSE </td>
#>    <td style="text-align:center;"> 51.91 </td>
#>   </tr>
#> </tbody>
#> </table>


# Panelsummary with fixest -------------------------
if (FALSE) {
ols_1 <- mtcars |> fixest::feols(mpg ~  cyl | gear + carb, cluster = ~hp, nthreads = 2)

panelsummary(ols_1, ols_1, mean_dependent = TRUE,
            panel_labels = c("Panel A:MPG", "Panel B: DISP"),
            caption = "The effect of cyl on MPG and DISP",
            italic = TRUE, stars = TRUE)


## Collapsing fixed effects (fixest-only)----------------

panelsummary(ols_1, ols_1, mean_dependent = TRUE,
            collapse_fe = TRUE, panel_labels = c("Panel A: MPG", "Panel B: DISP"),
            caption = "The effect of cyl on MPG and DISP",
            italic = TRUE, stars = TRUE)

## Including multiple models------------------


panelsummary(list(ols_1, ols_1, ols_1), ols_1,
             panel_labels = c("Panel A: MPG", "Panel B: DISP"),
              caption = "Multiple models",
              stars = TRUE)

}