This package provides publication-quality regression tables for use with FixedEffectModels.jl, GLM.jl, GLFixedEffectModels.jl and MixedModels.jl, as well as any package that implements the RegressionModel abstraction.
In its objective it is similar to (and heavily inspired by) the Stata command esttab and the R package stargazer.
- RegressionTables.jl
To install the package, type in the Julia command prompt
] add RegressionTablesusing RegressionTables, DataFrames, FixedEffectModels, RDatasets, GLM
df = dataset("datasets", "iris")
rr1 = reg(df, @formula(SepalLength ~ SepalWidth + fe(Species)))
rr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(Species)))
rr3 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + PetalWidth + fe(Species)))
rr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(Species)))
rr5 = glm(@formula(SepalWidth < 2.9 ~ PetalLength + PetalWidth + Species), df, Binomial())
regtable(
rr1,rr2,rr3,rr4,rr5;
render = AsciiTable(),
labels = Dict(
"versicolor" => "Versicolor",
"virginica" => "Virginica",
"PetalLength" => "Petal Length",
),
regression_statistics = [
Nobs => "Obs.",
R2,
R2Within,
PseudoR2 => "Pseudo-R2",
],
extralines = [
["Main Coefficient", "SepalWidth", "SepalWidth", "Petal Length", "Petal Length", "Intercept"],
DataRow(["Coef Diff", 0.372 => 2:3, 1.235 => 3:4, ""], align="lccr")
],
order = [r"Int", r" & ", r": "]
)yields
----------------------------------------------------------------------------------------------------
SepalLength SepalWidth SepalWidth < 2.9
-------------------------------------- ------------ ----------------
(1) (2) (3) (4) (5)
----------------------------------------------------------------------------------------------------
(Intercept) -1.917
(1.242)
SepalWidth & Petal Length -0.070
(0.041)
Species: Versicolor 10.441***
(1.957)
Species: Virginica 13.230***
(2.636)
SepalWidth 0.804*** 0.432*** 0.719***
(0.106) (0.081) (0.155)
Petal Length 0.776*** 1.047*** -0.188* -0.773
(0.064) (0.143) (0.083) (0.554)
PetalWidth -0.259 0.626*** -3.782**
(0.154) (0.123) (1.256)
SepalLength 0.378***
(0.066)
----------------------------------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes Yes
----------------------------------------------------------------------------------------------------
Estimator OLS OLS OLS OLS Binomial
----------------------------------------------------------------------------------------------------
Obs. 150 150 150 150 150
R2 0.726 0.863 0.870 0.635
Within-R2 0.281 0.642 0.659 0.391
Pseudo-R2 0.527 0.811 0.831 0.862 0.347
Main Coefficient SepalWidth SepalWidth Petal Length Petal Length Intercept
Coef Diff 0.372 1.235
----------------------------------------------------------------------------------------------------
LaTeX output can be generated by using
regtable(rr1,rr2,rr3,rr4; render = LatexTable())which yields
\begin{tabular}{lrrrr}
\toprule
& \multicolumn{3}{c}{SepalLength} & \multicolumn{1}{c}{SepalWidth} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-5}
& (1) & (2) & (3) & (4) \\
\midrule
SepalWidth & 0.804*** & 0.432*** & 0.719*** & \\
& (0.106) & (0.081) & (0.155) & \\
PetalLength & & 0.776*** & 1.047*** & -0.188* \\
& & (0.064) & (0.143) & (0.083) \\
PetalWidth & & & -0.259 & 0.626*** \\
& & & (0.154) & (0.123) \\
SepalWidth $\times$ PetalLength & & & -0.070 & \\
& & & (0.041) & \\
SepalLength & & & & 0.378*** \\
& & & & (0.066) \\
\midrule
SpeciesDummy Fixed Effects & Yes & Yes & Yes & Yes \\
\midrule
$N$ & 150 & 150 & 150 & 150 \\
$R^2$ & 0.726 & 0.863 & 0.870 & 0.635 \\
Within-$R^2$ & 0.281 & 0.642 & 0.659 & 0.391 \\
\bottomrule
\end{tabular}
Similarly, HTML tables can be created with HtmlTable().
Send the output to a text file by passing the destination file as a keyword argument:
regtable(rr1,rr2,rr3,rr4; render = LatexTable(), file="myoutputfile.tex")then use \input in LaTeX to include that file in your code. Be sure to use the booktabs package:
\documentclass{article}
\usepackage{booktabs}
\begin{document}
\begin{table}
\label{tab:mytable}
\input{myoutputfile}
\end{table}
\end{document}regtable() can also print TableRegressionModel's from GLM.jl (and output from other packages that produce TableRegressionModel's):
using GLM
dobson = DataFrame(Counts = [18.,17,15,20,10,20,25,13,12],
Outcome = categorical(repeat(["A", "B", "C"], outer = 3)),
Treatment = categorical(repeat(["a","b", "c"], inner = 3)))
rr1 = fit(LinearModel, @formula(SepalLength ~ SepalWidth), df)
lm1 = fit(LinearModel, @formula(SepalLength ~ SepalWidth), df)
gm1 = fit(GeneralizedLinearModel, @formula(Counts ~ 1 + Outcome + Treatment), dobson,
Poisson())
regtable(rr1,lm1,gm1)yields
---------------------------------------------
SepalLength Counts
------------------- --------
(1) (2) (3)
---------------------------------------------
(Intercept) 6.526*** 6.526*** 3.045***
(0.479) (0.479) (0.171)
SepalWidth -0.223 -0.223
(0.155) (0.155)
Outcome: B -0.454
(0.202)
Outcome: C -0.293
(0.193)
Treatment: b 0.000
(0.200)
Treatment: c -0.000
(0.200)
---------------------------------------------
Estimator OLS OLS Poisson
---------------------------------------------
N 150 150 9
R2 0.014 0.014
Pseudo R2 0.006 0.006 0.104
---------------------------------------------
Printing of StatsBase.RegressionModels (e.g., MixedModels.jl and GLFixedEffectModels.jl) generally works but are less well tested; please file as issue if you encounter problems printing them.
rr::FixedEffectModel...are theFixedEffectModels fromFixedEffectModels.jlthat should be printed. Only required argument.keepis aVectorof regressor names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all regressors will be shown.dropis aVectorof regressor names (Strings), integers, ranges or regex that should not be shown. Defaults to an empty vector, in which case no regressors will be dropped.orderis aVectorof regressor names (Strings), integers, ranges or regex that should be shown in that order. Defaults to an empty vector, in which case the order of regressors will be unchanged. Other regressors are still shown (assumingdropis empty)fixedeffectsis aVectorof FE names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all FE's will be shown.alignis aSymbolfrom the set[:l,:c,:r]indicating the alignment of results columns (default:rright-aligned). Currently works only with ASCII and LaTeX output.header_alignis aSymbolfrom the set[:l,:c,:r]indicating the alignment of the header row (default:ccentered). Currently works only with ASCII and LaTeX output.labelsis aDictthat contains displayed labels for variables (Strings) and other text in the table. If no label for a variable is found, it default to variable names. See documentation for special values.estimformatis aStringthat describes the format of the estimate.digitsis anIntthat describes the precision to be shown in the estimate. Defaults tonothing, which means the default (3) is used (default can be changed by settingRegressionTables.default_digits(render::AbstractRenderType, x) = 3).statisticformatis aStringthat describes the format of the number below the estimate (se/t).digits_statsis anIntthat describes the precision to be shown in the statistics. Defaults tonothing, which means the default (3) is used (default can be changed by settingRegressionTables.default_digits(render::AbstractRenderType, x) = 3).below_statisticis a type that describes a statistic that should be shown below each point estimate. Recognized values arenothing,StdError,TStat, andConfInt.nothingsuppresses the line. Defaults toStdError.regression_statisticsis aVectorof types that describe statistics to be shown at the bottom of the table. Built in recognized types areNobs,R2,PseudoR2,R2CoxSnell,R2Nagelkerke,R2Deviance,AdjR2,AdjPseudoR2,AdjR2Deviance,DOF,LogLikelihood,AIC,AICC,BIC,FStat,FStatPValue,FStatIV,FStatIVPValue, R2Within. Defaults vary based on regression inputs (simple linear model is [Nobs, R2]).extralinesis aVectoror aVector{<:AbsractVector}that will be added to the end of the table. A single vector will be its own row, a vector of vectors will each be a row. Defaults tonothing.number_regressionsis aBoolthat governs whether regressions should be numbered. Defaults totrue.groupsis aVector,Vector{<:AbstractVector}orMatrixof labels used to group regressions. This can be useful if results are shown for different data sets or sample restrictions.print_fe_sectionis aBoolthat governs whether a section on fixed effects should be shown. Defaults totrue.print_estimator_sectionis aBoolthat governs whether to print a section on which estimator (OLS/IV/Binomial/Poisson...) is used. Defaults totrueif more than one value is displayed.standardize_coefis aBoolthat governs whether the table should show standardized coefficients. Note that this only works withTableRegressionModels, and that only coefficient estimates and thebelow_statisticare being standardized (i.e. the R^2 etc still pertain to the non-standardized regression).render::AbstractRenderTypeis aAbstractRenderTypetype that governs how the table should be rendered. Standard supported types are ASCII (viaAsciiTable()) and LaTeX (viaLatexTable()). Defaults toAsciiTable().fileis aStringthat governs whether the table should be saved to a file. Defaults tonothing.transform_labelsis aDictor one of theSymbols:ampersand,:underscore,:underscore2space,:latex
A typical use is to pass a number of FixedEffectModels to the function, along with how it should be rendered (with render argument):
regtable(regressionResult1, regressionResult2; render = AsciiTable())Pass a string to the file argument to create or overwrite a file. For example, using LaTeX output,
regtable(regressionResult1, regressionResult2; render = LatexTable(), file="myoutfile.tex")Version 0.6 was a major rewrite of the backend with the goal of increasing the flexibility and decreasing the dependencies on other packages (regression packages are now extensions). While most code written with v0.5 should continue to run, there might be a few differences and some deprecation warnings. Below is a brief overview of the changes:
- There is an
extralinesargument that can accept vectors with pairs, where the pair defines a multicolumn value (["Label", "two columns" => 2:3, 1.5 => 4:5]), it can also accept aDataRowobject that allows for more control. - New
keepdropandorderarguments allow exact names, regex to search within names, integers to select specific values, and ranges (1:4) to select groups, and they can be mixed ([1:2, :end, r"Width"]) labelsnow applies to individual parts of an interaction or categorical coefficient name (hopefully reducing the number of labels required)- The interaction symbol now depends on the table type, so in Latex, the interactions will have
\$\\times\$- Using a Latex table will also automatically escape parts of coefficient names (if no other labels are provided)
- A confidence interval is now an option for a below statistic (
below_statistic=ConfInt) - Several defaults are different to try and provide more relevant information (see changes do defaults section)
- Fixed effect values now have a suffix (defaults to
" Fixed Effects") so that labeling can be simpler. Disable by settingprint_fe_suffix=false - It is now possible to print the coefficient value and "under statistic" on the same line (
stat_below=false) - It is possible to define custom regression statistics that are calculated based on the regressions provided
- It is possible to change the order of the major blocks in a regression table
- Using RegressionTables for descriptive statistics is now easier. Describe a DataFrame (
df_described=describe(df)) and provide that to a RegressionTable (tab = RegressionTable(names(df_described), Matrix(df_described))), there are also options to render the table as aLatexTableorHtmlTable. Write this to a file usingwrite(file_name, tab) - It is possible to overwrite almost any setting. For example, to make T-Statistics the default in all tables, run
RegressionTables.default_below_statistic(render::AbstractRenderType)=TStat - Option to show clustering (
print_clusters=true).- Can also be the size of the clusters by running
Base.repr(render::AbstractRenderType, x::RegressionTables.ClusterValue; args...) = repr(render, value(x); args...)
- Can also be the size of the clusters by running
- Several new regression statistics are now available, the full list is:
[Nobs, R2, PseudoR2, R2CoxSnell, R2Nagelkerke, R2Deviance, AdjR2, AdjPseudoR2, AdjR2Deviance, DOF, LogLikelihood, AIC, AICC, BIC, FStat, FStatPValue, FStatIV, FStatIVPValue, R2Within] - Use
LatexTableStarto create a table that expands the entire text width
There are some changes to the defaults from version 0.5 and two additional settings
- Interactions in coefficients now vary based on the type of table. In Latex, this now defaults to
$\\times$and in HTML×. These can be changed by running:RegressionTables.interaction_combine(render::AbstractRenderType) = " & "RegressionTables.interaction_combine(render::AbstractLatex) = " & "RegressionTables.interaction_combine(render::AbstractHtml) = " & "
print_estimatordefault wastrue, now it istrueif more than one type of regression is provided (i.e., "IV" and "OLS" will display the estimator, all "OLS" will not). Set to the old default by running:RegressionTables.default_print_estimator(x::AbstractRenderType, rrs) = true
number_regressionsdefault wastrue, now it istrueif more than one regression is provided. Set to the old default by running:RegressionTables.default_number_regressions(x::AbstractRenderType, rrs) = true
regression_statisticsdefault was[Nobs, R2], these will vary based on provided regressions. For example, a fixed effect regression will default to[Nobs, R2, R2Within]and a Probit regression will default to[Nobs, PseudoR2](and if multiple types, these will be combined). Set to the old default by running:RegressionTables.default_regression_statistics(x::AbstractRenderType, rrs::Tuple) = [Nobs, R2]
- Labels for the type of the regression are more varied for non-linear cases, instead of "NL", it will display "Poisson", "Probit", etc. These can be changed by running:
RegressionTables.label_distribution(x::AbstractRenderType, d::Probit) = "NL"
print_fe_suffixis a new setting where" Fixed Effect"is added after the fixed effect. Turn this off for all tables by running:RegressionTables.default_print_fe_suffix(x::AbstractRenderType) = false
print_control_indicatoris a new setting where a line is added if any coefficients are omitted. Turn this off for all tables by running:RegressionTables.default_print_control_indicator(x::AbstractRenderType) = false
Labels for most display elements around the table are no longer handled by the labels dictionary but by functions. The goal is to allow a "set and forget" mentality, where changing the label once permanently changes it for all tables. For example, instead of:
labels=Dict(
"__LABEL_ESTIMATOR__" => "Estimator",
"__LABEL_FE_YES__" => "Yes",
"__LABEL_FE_NO__" => "",
"__LABEL_ESTIMATOR_OLS" => "OLS",
"__LABEL_ESTIMATOR_IV" => "IV",
"__LABEL_ESTIMATOR_NL" => "NL"
)Run
RegressionTables.label(render::AbstractRenderType, ::Type{RegressionType}) = "Estimator"
RegressionTables.fe_value(render::AbstractRenderType, v) = v ? "Yes" : ""
RegressionTables.label_ols(render::AbstractRenderType) = "OLS"
RegressionTables.label_iv(render::AbstractRenderType) = "IV"
RegressionTables.label_distribution(render::AbstractRenderType, d::Probit) = "Probit"# non-linear values now
# display distribution instead of "NL"See the documentation for more examples. For regression statistics, it is possible to pass a pair (e.g., [Nobs => "Obs.", R2 => "R Squared"]) to relabel those.
Labels for coefficient names are the same, but interaction and categorical terms might see some differences. Now, each part of an interaction or categorical term can be labeled independently (so labels=Dict("coef1" => "Coef 1", "coef2" => "Coef 2") would relabel coef1 & coef2 to Coef 1 & Coef 2). This might cause changes to tables if the labels dictionary contains an interaction label but not both pieces independently, the display would depend on which order the dictionary is applied (so labels=Dict("coef1" => "Coef 1", "coef1 & coef2" => "Coef 1 & Coef 2") might turn the interaction into either Coef 1 & Coef 2 or Coef 1 & coef2).
The custom_statistics argument took a NamedTuple with vectors, this is now simplified in the extralines argument to a Vector, where the first argument is what is displayed in the left most column. extralines now accepts a Pair of val => cols (e.g., 0.153 => 2:3), where the second value creates a multicolumn display. See the examples in the documentation under "Extralines".
For statistics that can use the values in the regression model (e.g., the mean of Y), it is possible to create those under an AbstractRegressionStatistic. See the documentation for an example.
print_result is no longer necessary since an object is returned by the regtable function (which is editable) and displays well in notebooks like Pluto or Jupyter. Similarly for out_buffer, use tab=regtable(...); print(io, tab).
renderSettingsis deprecated, userenderandfileregressorsis deprecated, usekeepdropandorder