# Create a regression data.frame to manually edit further

Source:`R/panelsummary_raw.R`

`panelsummary_raw.Rd`

`panelsummary_raw` Creates a data.frame for further editing. The data.frame can be directly passed into kableExtra::kbl(), or alternatively, passed into panelsummary::clean_raw() to get typical defaults from kableExtra::kbl().

## Usage

```
panelsummary_raw(
...,
mean_dependent = FALSE,
colnames = NULL,
caption = NULL,
format = NULL,
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

- ...
all other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.

parameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:

`standardize`

,`centrality`

,`dispersion`

,`test`

,`ci_method`

,`prior`

,`diagnostic`

,`rope_range`

,`power`

,`cluster`

, etc.

performance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:

`metrics`

,`estimator`

, etc.

kableExtra::kbl or gt::gt draw tables, depending on the value of the

`output`

argument.

- 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.

- 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.

## Examples

```
## Using panelsummary_raw
ols_1 <- lm(mpg ~ hp + cyl, data = mtcars)
panelsummary_raw(ols_1, ols_1)
#> term Model 1
#> 1 (Intercept) 36.908
#> 2 (2.191)
#> 3 hp -0.019
#> 4 (0.015)
#> 5 cyl -2.265
#> 6 (0.576)
#> 7 Num.Obs. 32
#> 8 R2 0.741
#> 9 R2 Adj. 0.723
#> 10 AIC 169.6
#> 11 BIC 175.4
#> 12 Log.Lik. -80.781
#> 13 RMSE 3.02
#> 14 (Intercept) 36.908
#> 15 (2.191)
#> 16 hp -0.019
#> 17 (0.015)
#> 18 cyl -2.265
#> 19 (0.576)
#> 20 Num.Obs. 32
#> 21 R2 0.741
#> 22 R2 Adj. 0.723
#> 23 AIC 169.6
#> 24 BIC 175.4
#> 25 Log.Lik. -80.781
#> 26 RMSE 3.02
## Including multiple models------------------
panelsummary_raw(list(ols_1, ols_1, ols_1), ols_1,
caption = "Multiple models",
stars = TRUE)
#> term Model 1 Model 2 Model 3
#> 1 (Intercept) 36.908*** 36.908*** 36.908***
#> 2 (2.191) (2.191) (2.191)
#> 3 hp -0.019 -0.019 -0.019
#> 4 (0.015) (0.015) (0.015)
#> 5 cyl -2.265*** -2.265*** -2.265***
#> 6 (0.576) (0.576) (0.576)
#> 7 Num.Obs. 32 32 32
#> 8 R2 0.741 0.741 0.741
#> 9 R2 Adj. 0.723 0.723 0.723
#> 10 AIC 169.6 169.6 169.6
#> 11 BIC 175.4 175.4 175.4
#> 12 Log.Lik. -80.781 -80.781 -80.781
#> 13 RMSE 3.02 3.02 3.02
#> 14 (Intercept) 36.908***
#> 15 (2.191)
#> 16 hp -0.019
#> 17 (0.015)
#> 18 cyl -2.265***
#> 19 (0.576)
#> 20 Num.Obs. 32
#> 21 R2 0.741
#> 22 R2 Adj. 0.723
#> 23 AIC 169.6
#> 24 BIC 175.4
#> 25 Log.Lik. -80.781
#> 26 RMSE 3.02
```