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
outputargument.
- 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
modelsummaryfunction.Integer: Number of decimal digits
User-supplied functions:
Any function which accepts a numeric vector and returns a character vector of the same length.
modelsummaryfunctions:fmt = fmt_significant(2): Two significant digits (at the term-level)fmt = fmt_decimal(digits = 2, pdigits = 3): Decimal digits for estimate and p valuesfmt = fmt_sprintf("%.3f"): See?sprintffmt = fmt_term("(Intercept)" = 1, "X" = 2): Format terms differentlyfmt = 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 thesiunitxpackage 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 thegluepackage format. Examples:"estimate""{estimate} ({std.error}){stars}""{estimate} [{conf.low}, {conf.high}]"
- statistic
vector of strings or
gluestrings 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)gluepackage 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
glueexample, users must setfmt=NULL.Parentheses are added automatically unless the string includes
gluecurly 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
vcovargument 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 thesandwichpackage, or equivalent method. The behavior of those functions can (and sometimes must) be altered by passing arguments tosandwichdirectly frommodelsummarythrough 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,
modelsummarycannot automatically calculate p values. Thestarsargument may thus use incorrect significance thresholds whenvcovis a list of vectors.
- conf_level
numeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to
NULLdoes 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
estimateorstatisticarguments 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_mapcan be a named or an unnamed character vector. Ifcoef_mapis 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_omitis 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
modelsummarypackage supplies acoef_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_mapdictionary 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 byget_gof(model). Themodelsummary::gof_mapdefault 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