Exporting estimation tables
Laurent Berge
2024-06-12
fixest offers a tool, the function etable,
to view estimation tables in R or export them to Latex.
The main advantage of this function is its simplicity: it is
completely integrated with other fixest functions, making
it exceedingly easy to export multiple estimation results with, say,
different types of standard-errors. On the other hand, its main
limitations are that i) only fixest objects can be
exported, and ii) only Latex is supported (although the use of
post-processing functions opens up a lot of possibilities).
It also offers a fair deal of customization, and since you can seamlessly change its default values, you can completely transform the style of your tables without modifying a single line of code.
Note that there exists excellent alternatives to export tables, like
for instance modelsummary
(if you don’t know it already, please do have a look, it’s really
great!); but they are less integrated with fixest objects,
possibly necessitating more lines of code to export the same
results.
This document does not describe etable’s arguments in
details (the help page provides many examples). Rather, it illustrates
some features that may be hidden at first sight.
This document applies to fixest version 0.10.2 or
higher.
Preliminaries
Throughout this document, we will use data from the
airquality data base. We also set a dictionary that will be
used to rename the variables used in etable. This
dictionary is set once and for all.
Exporting multiple estimations to data.frames
Let’s estimate the following four models and cluster the
standard-errors by Day:
# On multiple estimations: see the dedicated vignette
est = feols(Ozone ~ Solar.R + sw0(Wind + Temp) | csw(Month, Day),
airquality, cluster = ~Day)By default, when the argument file is missing, the
function etable returns a data.frame. Let’s
see the output of the previous estimations:
#> est.1 est.2 est.3
#> Dependent Var.: Ozone (ppb) Ozone (ppb) Ozone (ppb)
#>
#> Solar Radiation (Langleys) 0.115*** (0.023) 0.052* (0.020) 0.108** (0.033)
#> Wind Speed (mph) -3.11*** (0.799)
#> Temperature 1.88*** (0.367)
#> Fixed-Effects: ---------------- ---------------- ---------------
#> Month Yes Yes Yes
#> Day No No Yes
#> __________________________ ________________ ________________ _______________
#> S.E.: Clustered by: Day by: Day by: Day
#> Observations 111 111 111
#> R2 0.31974 0.63686 0.58018
#> Within R2 0.12245 0.53154 0.12074
#>
#> est.4
#> Dependent Var.: Ozone (ppb)
#>
#> Solar Radiation (Langleys) 0.051* (0.024)
#> Wind Speed (mph) -3.29*** (0.778)
#> Temperature 2.05*** (0.242)
#> Fixed-Effects: ----------------
#> Month Yes
#> Day Yes
#> __________________________ ________________
#> S.E.: Clustered by: Day
#> Observations 111
#> R2 0.81604
#> Within R2 0.61471
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1What can we notice? First, the variables are properly labeled. Second, the fixed-effects section details which fixed-effects is included in which model. Third, the type of standard-error is reminded in a dedicated row.
Starting from this table, two elements are detailed: a) how to change
the look of the table with the style.df argument, b) how to
leverage tools from other packages with the postprocess.df
argument.
Changing the look of the data.frame with style.df
You can change many elements of the data.frame with the
argument style.df whose input must come from the function
style.df. The style monitors many elements of the table, in
particular the titles of the sections. Let’s have an example:
etable(est, style.df = style.df(depvar.title = "", fixef.title = "",
fixef.suffix = " fixed effect", yesNo = "yes"))#> est.1 est.2 est.3
#> Ozone (ppb) Ozone (ppb) Ozone (ppb)
#>
#> Solar Radiation (Langleys) 0.115*** (0.023) 0.052* (0.020) 0.108** (0.033)
#> Wind Speed (mph) -3.11*** (0.799)
#> Temperature 1.88*** (0.367)
#> Month fixed effect yes yes yes
#> Day fixed effect yes
#> __________________________ ________________ ________________ _______________
#> S.E.: Clustered by: Day by: Day by: Day
#> Observations 111 111 111
#> R2 0.31974 0.63686 0.58018
#> Within R2 0.12245 0.53154 0.12074
#>
#> est.4
#> Ozone (ppb)
#>
#> Solar Radiation (Langleys) 0.051* (0.024)
#> Wind Speed (mph) -3.29*** (0.778)
#> Temperature 2.05*** (0.242)
#> Month fixed effect yes
#> Day fixed effect yes
#> __________________________ ________________
#> S.E.: Clustered by: Day
#> Observations 111
#> R2 0.81604
#> Within R2 0.61471
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1In the previous example, the dependent variable and fixed-effects
(FE) headers have been removed, and this is achieved with the (explicit)
arguments depvar.title and fixef.title.
Furthermore the suffix "fixed effect" is added to each
fixed-effect variable, and the indicator of which FE is included in
which model is slightly changed. There are more options that are
described in the style.df documentation.
Postprocessing with external functions
Since the output of etable is a data.frame,
any formatting function handling data.frames can be
leveraged. It is then very easy to integrate it into
etable. Let’s have an example with the package
pander:
#>
#>
#> | | est.1 | est.2 |
#> |:--------------------------:|:----------------:|:----------------:|
#> | Dependent Var.: | Ozone (ppb) | Ozone (ppb) |
#> | | | |
#> | Solar Radiation (Langleys) | 0.115*** (0.023) | 0.052* (0.020) |
#> | Wind Speed (mph) | | -3.11*** (0.799) |
#> | Temperature | | 1.88*** (0.367) |
#> | Fixed-Effects: | ---------------- | ---------------- |
#> | Month | Yes | Yes |
#> | Day | No | No |
#> | __________________________ | ________________ | ________________ |
#> | S.E.: Clustered | by: Day | by: Day |
#> | Observations | 111 | 111 |
#> | R2 | 0.31974 | 0.63686 |
#> | Within R2 | 0.12245 | 0.53154 |
#>
#> Table: Table continues below
#>
#>
#>
#> | est.3 | est.4 |
#> |:---------------:|:----------------:|
#> | Ozone (ppb) | Ozone (ppb) |
#> | | |
#> | 0.108** (0.033) | 0.051* (0.024) |
#> | | -3.29*** (0.778) |
#> | | 2.05*** (0.242) |
#> | --------------- | ---------------- |
#> | Yes | Yes |
#> | Yes | Yes |
#> | _______________ | ________________ |
#> | by: Day | by: Day |
#> | 111 | 111 |
#> | 0.58018 | 0.81604 |
#> | 0.12074 | 0.61471 |What did it do? First, it called the function
pandoc.table.return from within etable.
Second, the argument style is not from etable
but is from pander’s function. Indeed, all the arguments to
the postprocessing function are caught and passed to it. So far so good.
But you could say: why bother using the posprocessing function when we
could just use piping? You’re right, but wait a second for the next
section.
Setting etable default values
One important feature of etable is that you can set the
default values of almost all its arguments. This includes the
postprocessing function. Let’s change the default values of
style.df and postprocess.df:
my_style = style.df(depvar.title = "", fixef.title = "",
fixef.suffix = " fixed effect", yesNo = "yes")
setFixest_etable(style.df = my_style, postprocess.df = pandoc.table.return)Since now pandoc.table.return is the default
postprocessing, all its arguments are added to
etable. So calls like that are valid even though
style or caption are not arguments
from etable:
#>
#>
#> | | est[rhs = 2].1 | est[rhs = 2].2 |
#> |:--------------------------:|:----------------:|:----------------:|
#> | | Ozone (ppb) | Ozone (ppb) |
#> | | | |
#> | Solar Radiation (Langleys) | 0.052* (0.020) | 0.051* (0.024) |
#> | Wind Speed (mph) | -3.11*** (0.799) | -3.29*** (0.778) |
#> | Temperature | 1.88*** (0.367) | 2.05*** (0.242) |
#> | Month fixed effect | yes | yes |
#> | Day fixed effect | | yes |
#> | __________________________ | ________________ | ________________ |
#> | S.E.: Clustered | by: Day | by: Day |
#> | Observations | 111 | 111 |
#> | R2 | 0.63686 | 0.81604 |
#> | Within R2 | 0.53154 | 0.61471 |
#>
#> Table: New default valuesExporting multiple estimations to Latex
We now illustrate the exports to Latex. First, to include all the sections of a table, let’s add a fifth estimation to the previous example; this new estimation includes variables with varying slopes:
To export to Latex, use the argument tex = TRUE (note
that this argument is on when the argument file is not
missing):
The previous code produces the following table:
The style of the table is rather sober, but no worries: most of it
can be customized. We now illustrate: a) how to change the look of the
table with the argument style.tex, and how to include
custom features with the argument postproces.tex.
Changing the look of the Latex table with
style.tex
The argument style.tex defines how the table looks. It
allows an in-depth customization of the table. The table is split into
several components, each allowing some customization. The components of
a table and some of its associated keywords are described by the
following figure:
The argument style.tex only accepts outputs from the
function style.tex. That function is documented and
describes the different components that can be found in the previous
illustration.
The function style.tex has two starting points (in the
argument main), either the style of the first table
displayed, either a much more compact style named “aer”. Let’s show the
same table with the aer style, without stars beside the coefficients,
and different fit statistics:
etable(est, est_slopes, style.tex = style.tex("aer"),
signif.code = NA, fitstat = ~ r2 + n, tex = TRUE)Which yields the following table:
Adding custom features with postprocess.tex
When tex = TRUE etable returns a character
vector. It is possible to modify it at will with the argument
postprocess.tex. When a postprocessing function is
detected, two additional tags are added to the character vector
identifying the start and end of the table ("%start:tab\\n"
and "%end:tab\\n").
Assume we want to set the rule widths of the table, we could write the following function:
set_rules = function(x, heavy, light){
# x: the character vector returned by etable
tex2add = ""
if(!missing(heavy)){
tex2add = paste0("\\setlength\\heavyrulewidth{", heavy, "}\n")
}
if(!missing(light)){
tex2add = paste0(tex2add, "\\setlength\\lightrulewidth{", light, "}\n")
}
if(nchar(tex2add) > 0){
x[x == "%start:tab\n"] = tex2add
}
x
}Now we can summon that function from etable:
Of course it is even more convenient to set set_rules as
the default postprocessing function.
Setting etable default values: Latex edition
To set the default values, like for the data.frame output, use
setFixest_etable:
setFixest_etable(style.tex = style.tex("aer", signif.code = NA), postprocess.tex = set_rules,
fitstat = ~ r2 + n)Now we can access directly the arguments of the postprocessing function and the default style is the one of the second table:
#> \begingroup
#> \centering
#> \setlength\heavyrulewidth{0.14em}
#> \begin{tabular}{lcccc}
#> \toprule
#> & \multicolumn{4}{c}{Ozone (ppb)}\\
#> & (1) & (2) & (3) & (4)\\
#> \midrule
#> Solar Radiation (Langleys) & 0.115 & 0.052 & 0.108 & 0.051\\
#> & (0.023) & (0.020) & (0.033) & (0.024)\\
#> Wind Speed (mph) & & -3.11 & & -3.29\\
#> & & (0.799) & & (0.778)\\
#> Temperature & & 1.88 & & 2.05\\
#> & & (0.367) & & (0.242)\\
#> \\
#> R$^2$ & 0.31974 & 0.63686 & 0.58018 & 0.81604\\
#> Observations & 111 & 111 & 111 & 111\\
#> \\
#> Month fixed effects & $\checkmark$ & $\checkmark$ & $\checkmark$ & $\checkmark$\\
#> Day fixed effects & & & $\checkmark$ & $\checkmark$\\
#> \bottomrule
#> \end{tabular}
#> \par\endgroupSetting custom fit statistics
It is often useful to include in a table some fit statistics that are
not standard, or simply that may not be included in fixest
built-in fit statistics. While it is possible to include any extra line
in the table with the argument extralines, this is rather
cumbersome and possibly error-prone if this task has to be repeated.
To avoid that kind of issue, fixest allows the user to
register custom fit statistics. Once they are registered, they can be
seamlessly called via the fitstat argument in
etable.
Let’s continue with the previous example using the
airquality data set, and now let’s display different
p-values of statistical significance for the variable
Solar.R. These p-values will vary depending on how we
compute the VCOV matrix.
Here is an example that will shortly be explained:
fitstat_register(type = "p_s", alias = "pvalue (standard)",
fun = function(x) pvalue(x, vcov = "iid")["Solar.R"])
fitstat_register(type = "p_h", alias = "pvalue (Heterosk.)",
fun = function(x) pvalue(x, vcov = "hetero")["Solar.R"])
fitstat_register(type = "p_day", alias = "pvalue (Day)",
fun = function(x) pvalue(x, vcov = ~Day)["Solar.R"])
fitstat_register(type = "p_month", alias = "pvalue (Month)",
fun = function(x) pvalue(x, vcov = ~Month)["Solar.R"])
# We first reset the default values set in the previous sections
setFixest_etable(reset = TRUE)
# Now we display the results with the new fit statistics
etable(est, fitstat = ~ . + p_s + p_h + p_day + p_month)#> est.1 est.2
#> Dependent Var.: Ozone (ppb) Ozone (ppb)
#>
#> Solar Radiation (Langleys) 0.1148*** (0.0234) 0.0522* (0.0202)
#> Wind Speed (mph) -3.109*** (0.7986)
#> Temperature 1.875*** (0.3671)
#> Fixed-Effects: ------------------ ------------------
#> Month Yes Yes
#> Day No No
#> __________________________ __________________ __________________
#> S.E.: Clustered by: Day by: Day
#> Observations 111 111
#> R2 0.31974 0.63686
#> Within R2 0.12245 0.53154
#> pvalue (standard) 0.00022 0.02957
#> pvalue (Heterosk.) 6.64e-6 0.02268
#> pvalue (Day) 3e-5 0.01468
#> pvalue (Month) 0.06066 0.26992
#>
#> est.3 est.4
#> Dependent Var.: Ozone (ppb) Ozone (ppb)
#>
#> Solar Radiation (Langleys) 0.1078** (0.0329) 0.0509* (0.0236)
#> Wind Speed (mph) -3.289*** (0.7777)
#> Temperature 2.052*** (0.2415)
#> Fixed-Effects: ----------------- ------------------
#> Month Yes Yes
#> Day Yes Yes
#> __________________________ _________________ __________________
#> S.E.: Clustered by: Day by: Day
#> Observations 111 111
#> R2 0.58018 0.81604
#> Within R2 0.12074 0.61471
#> pvalue (standard) 0.00196 0.03720
#> pvalue (Heterosk.) 0.00103 0.02815
#> pvalue (Day) 0.00263 0.03881
#> pvalue (Month) 0.02676 0.29967
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The function fitstat_register is a tool to add fit
statistics in the fitstat engine. The first argument,
type, is the code name by which the statistic is to be
summoned. The argument alias provides the row name of the
statistics: how it should look in the table. Finally in the
fun argument is the function computing the statistic. That
function must apply to a fixest estimation and must also
return a single value.
Once these statistics are registered, they can seamlessly be summoned
with the argument fitstat and will appear in the order the
user provide. Note that the dot in
fitstat = ~ . + p_s + etc represents the default statistics
to be displayed and need not be there.
Using external functions to export multiple results
Thanks to contributors (namely Karl Dunkle Werner),
fixest objects are compatible with broom methods. This
means that export functions building on broom can be
leveraged, like for instance the excellent modelsummary.
In case you use external export tools, here are some tips.
Multiple estimations, like the object est in the
previous examples, are a bit special and broom methods
can’t apply directly. To export them, you first need to coerce the
results into a list: by simply using as.list(est).
By default in fixest the estimations are separated from
the calculation of the VCOV matrices. That’s not a problem when using
etable since, after providing the argument se
or cluster, all VCOVs are calculated at once. Using other
tools for exportation requires a call to summary for each
model to compute the appropriate standard-errors. But this process can
also be automatized. The function .l() can be used to
coerce several fixest objects to which summary
can then be applied, for example:
The previous code returns a list of the five estimations for which the standard-errors are all clustered at the Month level, and can then be exported with external software.