Aggregates the results of multiple estimations and displays them in the form of either a Latex table or a data.frame. Note that you will need the booktabs package for the Latex table to render properly.

etable(
  ...,
  vcov = NULL,
  stage = 2,
  agg = NULL,
  se = NULL,
  ssc = NULL,
  cluster = NULL,
  .vcov,
  .vcov_args = NULL,
  digits = 4,
  digits.stats = 5,
  tex,
  fitstat,
  title,
  coefstat = "se",
  ci = 0.95,
  se.row = NULL,
  se.below = NULL,
  keep,
  drop,
  order,
  dict,
  file,
  replace = FALSE,
  convergence,
  signifCode,
  label,
  float,
  headers = list("auto"),
  fixef_sizes = FALSE,
  fixef_sizes.simplify = TRUE,
  keepFactors = TRUE,
  family,
  powerBelow = -5,
  interaction.combine = NULL,
  interaction.order = NULL,
  i.equal = NULL,
  depvar = TRUE,
  style.tex = NULL,
  style.df = NULL,
  notes = NULL,
  group = NULL,
  extraline = NULL,
  fixef.group = NULL,
  placement = "htbp",
  drop.section = NULL,
  poly_dict = c("", " square", " cube"),
  postprocess.tex = NULL,
  postprocess.df = NULL,
  fit_format = "__var__",
  coef.just = NULL,
  meta = NULL,
  meta.time = NULL,
  meta.author = NULL,
  meta.sys = NULL,
  meta.call = NULL,
  meta.comment = NULL
)

setFixest_etable(
  digits = 4,
  digits.stats = 5,
  fitstat,
  coefstat = c("se", "tstat", "confint"),
  ci = 0.95,
  se.below = TRUE,
  keep,
  drop,
  order,
  dict,
  signifCode,
  float,
  fixef_sizes = FALSE,
  fixef_sizes.simplify = TRUE,
  family,
  powerBelow = -5,
  interaction.order = NULL,
  depvar,
  style.tex = NULL,
  style.df = NULL,
  notes = NULL,
  group = NULL,
  extraline = NULL,
  fixef.group = NULL,
  placement = "htbp",
  drop.section = NULL,
  postprocess.tex = NULL,
  postprocess.df = NULL,
  fit_format = "__var__",
  meta.time = NULL,
  meta.author = NULL,
  meta.sys = NULL,
  meta.call = NULL,
  meta.comment = NULL,
  reset = FALSE,
  save = FALSE
)

getFixest_etable()

esttable(
  ...,
  vcov = NULL,
  stage = 2,
  agg = NULL,
  se = NULL,
  ssc = NULL,
  cluster = NULL,
  .vcov,
  .vcov_args = NULL,
  digits = 4,
  digits.stats = 5,
  fitstat,
  coefstat = "se",
  ci = 0.95,
  se.row = NULL,
  se.below = NULL,
  keep,
  drop,
  order,
  dict,
  file,
  replace = FALSE,
  convergence,
  signifCode,
  headers = list("auto"),
  fixef_sizes = FALSE,
  fixef_sizes.simplify = TRUE,
  keepFactors = TRUE,
  family,
  powerBelow = -5,
  interaction.combine = NULL,
  interaction.order = NULL,
  i.equal = NULL,
  depvar = TRUE,
  style.df = NULL,
  group = NULL,
  extraline = NULL,
  fixef.group = NULL,
  drop.section = NULL,
  poly_dict = c("", " square", " cube"),
  postprocess.df = NULL,
  fit_format = "__var__",
  coef.just = NULL
)

esttex(
  ...,
  vcov = NULL,
  stage = 2,
  agg = NULL,
  se = NULL,
  ssc = NULL,
  cluster = NULL,
  .vcov,
  .vcov_args = NULL,
  digits = 4,
  digits.stats = 5,
  fitstat,
  title,
  coefstat = "se",
  ci = 0.95,
  se.row = NULL,
  se.below = NULL,
  keep,
  drop,
  order,
  dict,
  file,
  replace = FALSE,
  convergence,
  signifCode,
  label,
  float,
  headers = list("auto"),
  fixef_sizes = FALSE,
  fixef_sizes.simplify = TRUE,
  keepFactors = TRUE,
  family,
  powerBelow = -5,
  interaction.combine = NULL,
  interaction.order = NULL,
  i.equal = NULL,
  depvar = TRUE,
  style.tex = NULL,
  notes = NULL,
  group = NULL,
  extraline = NULL,
  fixef.group = NULL,
  placement = "htbp",
  drop.section = NULL,
  poly_dict = c("", " square", " cube"),
  postprocess.tex = NULL,
  fit_format = "__var__",
  meta = NULL,
  meta.time = NULL,
  meta.author = NULL,
  meta.sys = NULL,
  meta.call = NULL,
  meta.comment = NULL
)

Arguments

...

Used to capture different fixest estimation objects (obtained with femlm, feols or feglm). Note that any other type of element is discarded. Note that you can give a list of fixest objects.

vcov

Versatile argument to specify the VCOV. In general, it is either a character scalar equal to a VCOV type, either a formula of the form: vcov_type ~ variables. The VCOV types implemented are: "iid", "hetero" (or "HC1"), "cluster", "twoway", "NW" (or "newey_west"), "DK" (or "driscoll_kraay"), and "conley". It also accepts object from vcov_cluster, vcov_NW, NW, vcov_DK, DK, vcov_conley and conley. It also accepts covariance matrices computed externally. Finally it accepts functions to compute the covariances. See the `vcov` documentation in the vignette.

stage

Can be equal to 2 (default), 1, 1:2 or 2:1. Only used if the object is an IV estimation: defines the stage to which summary should be applied. If stage = 1 and there are multiple endogenous regressors or if stage is of length 2, then an object of class fixest_multi is returned.

agg

A character scalar describing the variable names to be aggregated, it is pattern-based. All variables that match the pattern will be aggregated. It must be of the form "(root)", the parentheses must be there and the resulting variable name will be "root". You can add another root with parentheses: "(root1)regex(root2)", in which case the resulting name is "root1::root2". To name the resulting variable differently you can pass a named vector: c("name" = "pattern") or c("name" = "pattern(root2)"). It's a bit intricate sorry, please see the examples.

se

Character scalar. Which kind of standard error should be computed: “standard”, “hetero”, “cluster”, “twoway”, “threeway” or “fourway”? By default if there are clusters in the estimation: se = "cluster", otherwise se = "iid". Note that this argument is deprecated, you should use vcov instead.

ssc

An object of class ssc.type obtained with the function ssc. Represents how the degree of freedom correction should be done.You must use the function ssc for this argument. The arguments and defaults of the function ssc are: adj = TRUE, fixef.K="nested", cluster.adj = TRUE, cluster.df = "conventional", t.df = "conventional", fixef.force_exact=FALSE). See the help of the function ssc for details.

cluster

Tells how to cluster the standard-errors (if clustering is requested). Can be either a list of vectors, a character vector of variable names, a formula or an integer vector. Assume we want to perform 2-way clustering over var1 and var2 contained in the data.frame base used for the estimation. All the following cluster arguments are valid and do the same thing: cluster = base[, c("var1", "var2")], cluster = c("var1", "var2"), cluster = ~var1+var2. If the two variables were used as fixed-effects in the estimation, you can leave it blank with vcov = "twoway" (assuming var1 [resp. var2] was the 1st [res. 2nd] fixed-effect). You can interact two variables using ^ with the following syntax: cluster = ~var1^var2 or cluster = "var1^var2".

.vcov

A function to be used to compute the standard-errors of each fixest object. You can pass extra arguments to this function using the argument .vcov_args. See the example.

.vcov_args

A list containing arguments to be passed to the function .vcov.

digits

Integer or character scalar. Default is 4 and represents the number of significant digits to be displayed for the coefficients and standard-errors. To apply rounding instead of significance use, e.g., digits = "r3" which will round at the first 3 decimals. If character, it must be of the form "rd" or "sd" with d a digit (r is for round and s is for significance). For the number of digits for the fit statistics, use digits.stats. Note that when significance is used it does not exactly display the number of significant digits: see details for its exact meaning.

digits.stats

Integer or character scalar. Default is 5 and represents the number of significant digits to be displayed for the fit statistics. To apply rounding instead of significance use, e.g., digits = "r3" which will round at the first 3 decimals. If character, it must be of the form "rd" or "sd" with d a digit (r is for round and s is for significance). Note that when significance is used it does not exactly display the number of significant digits: see details for its exact meaning.

tex

Logical: whether the results should be a data.frame or a Latex table. By default, this argument is TRUE if the argument file (used for exportation) is not missing; it is equal to FALSE otherwise.

fitstat

A character vector or a one sided formula (both with only lowercase letters). A vector listing which fit statistics to display. The valid types are 'n', 'll', 'aic', 'bic' and r2 types like 'r2', 'pr2', 'war2', etc (see all valid types in r2). Also accepts valid types from the function fitstat. The default value depends on the models to display. Example of use: fitstat=c('n', 'cor2', 'ar2', 'war2'), or fitstat=~n+cor2+ar2+war2 using a formula. You can use the dot to refer to default values: ~ . + ll would add the log-likelihood to the default fit statistics.

title

(Tex only.) Character scalar. The title of the Latex table.

coefstat

One of "se" (default), "tstat" or "confint". The statistic to report for each coefficient: the standard-error, the t-statistics or the confidence interval. You can adjust the confidence interval with the argument ci.

ci

Level of the confidence interval, defaults to 0.95. Only used if coefstat = confint.

se.row

Logical scalar, default is NULL. Whether should be displayed the row with the type of standard-error for each model. When tex = FALSE, the default is TRUE. When tex = FALSE, the row is showed only when there is a table-footer and the types of standard-errors differ across models.

se.below

Logical or NULL (default). Should the standard-errors be displayed below the coefficients? If NULL, then this is TRUE for Latex and FALSE otherwise.

keep

Character vector. This element is used to display only a subset of variables. This should be a vector of regular expressions (see regex help for more info). Each variable satisfying any of the regular expressions will be kept. This argument is applied post aliasing (see argument dict). Example: you have the variable x1 to x55 and want to display only x1 to x9, then you could use keep = "x[[:digit:]]$". If the first character is an exclamation mark, the effect is reversed (e.g. keep = "!Intercept" means: every variable that does not contain “Intercept” is kept). See details.

drop

Character vector. This element is used if some variables are not to be displayed. This should be a vector of regular expressions (see regex help for more info). Each variable satisfying any of the regular expressions will be discarded. This argument is applied post aliasing (see argument dict). Example: you have the variable x1 to x55 and want to display only x1 to x9, then you could use drop = "x[[:digit:]]{2}". If the first character is an exclamation mark, the effect is reversed (e.g. drop = "!Intercept" means: every variable that does not contain “Intercept” is dropped). See details.

order

Character vector. This element is used if the user wants the variables to be ordered in a certain way. This should be a vector of regular expressions (see regex help for more info). The variables satisfying the first regular expression will be placed first, then the order follows the sequence of regular expressions. This argument is applied post aliasing (see argument dict). Example: you have the following variables: month1 to month6, then x1 to x5, then year1 to year6. If you want to display first the x's, then the years, then the months you could use: order = c("x", "year"). If the first character is an exclamation mark, the effect is reversed (e.g. order = "!Intercept" means: every variable that does not contain “Intercept” goes first). See details.

dict

A named character vector or a logical scalar. It changes the original variable names to the ones contained in the dictionary. E.g. to change the variables named a and b3 to (resp.) “$log(a)$” and to “$bonus^3$”, use dict=c(a="$log(a)$",b3="$bonus^3$"). By default, it is equal to getFixest_dict(), a default dictionary which can be set with setFixest_dict. You can use dict = FALSE to disable it. By default dict modifies the entries in the global dictionary, to disable this behavior, use "reset" as the first element (ex: dict=c("reset", mpg="Miles per gallon")).

file

A character scalar. If provided, the Latex (or data frame) table will be saved in a file whose path is file. If you provide this argument, then a Latex table will be exported, to export a regular data.frame, use argument tex = FALSE.

replace

Logical, default is FALSE. Only used if option file is used. Should the exported table be written in a new file that replaces any existing file?

convergence

Logical, default is missing. Should the convergence state of the algorithm be displayed? By default, convergence information is displayed if at least one model did not converge.

signifCode

Named numeric vector, used to provide the significance codes with respect to the p-value of the coefficients. Default is c("***"=0.01, "**"=0.05, "*"=0.10) for a Latex table and c("***"=0.001, "**"=0.01, "*"=0.05, "."=0.10) for a data.frame (to conform with R's default). To suppress the significance codes, use signifCode=NA or signifCode=NULL. Can also be equal to "letters", then the default becomes c("a"=0.01, "b"=0.05, "c"=0.10).

label

(Tex only.) Character scalar. The label of the Latex table.

float

(Tex only.) Logical. By default, if the argument title or label is provided, it is set to TRUE. Otherwise, it is set to FALSE.

headers

Character vector or list. Adds one or more header lines in the table. A header line can be represented by a character vector or a named list of numbers where the names are the cell values and the numbers are the span. Example: headers=list("M"=2, "F"=3) will create a row with 2 times "M" and three time "F" (this is identical to headers=rep(c("M", "F"), c(2, 3))). You can stack header lines within a list, in that case the list names will be displayed in the leftmost cell. Example: headers=list(Gender=list("M"=2, "F"=3), Country="US" will create two header lines. When tex = TRUE, you can add a rule to separate groups by using ":_:" somewhere in the row name (ex: headers=list(":_:Gender"=list("M"=2, "F"=3)). You can monitor the placement by inserting a special character in the row name: "^" means at the top, "-" means in the middle (default) and "_" means at the bottom. Example: headers=list("_Country"="US") will add the country row as the very last header row (after the model row). Finally, you can use the special value "auto" to include automatic headers when the data contains split sample estimations. By default it is equal to list("auto").

fixef_sizes

(Tex only.) Logical, default is FALSE. If TRUE and fixed-effects were used in the models, then the number of "units" per fixed-effect dimension is also displayed.

fixef_sizes.simplify

Logical, default is TRUE. Only used if fixef_sizes = TRUE. If TRUE, the fixed-effects sizes will be displayed in parentheses instead of in a separate line if there is no ambiguity (i.e. if the size is constant across models).

keepFactors

Logical, default is TRUE. If FALSE, then factor variables are displayed as fixed-effects and no coefficient is shown.

family

Logical, default is missing. Whether to display the families of the models. By default this line is displayed when at least two models are from different families.

powerBelow

(Tex only.) Integer, default is -5. A coefficient whose value is below 10**(powerBelow+1) is written with a power in Latex. For example 0.0000456 would be written 4.56$\times 10^{-5}$ by default. Setting powerBelow = -6 would lead to 0.00004 in Latex.

interaction.combine

Character scalar, defaults to " $\times$ " for Tex and to " = " otherwise. When the estimation contains interactions, then the variables names (after aliasing) are combined with this argument. For example: if dict = c(x1="Wind", x2="Rain") and you have the following interaction x1:x2, then it will be renamed (by default) Wind $\times$ Rain -- using interaction.combine = "*" would lead to Wind*Rain.

interaction.order

Character vector of regular expressions. Only affects variables that are interacted like x1 and x2 in feols(y ~ x1*x2, data). You can change the order in which the interacted variables are displayed: e.g. interaction.order = "x2" would lead to "x1 x x2" instead of "x1 x x2". Please look at the argument 'order' and the dedicated section in the help page for more information.

i.equal

Character scalar, defaults to " $=$ " when tex = TRUE and " = " otherwise. Only affects factor variables created with the function i, tells how the variable should be linked to its value. For example if you have the Species factor from the iris data set, by default the display of the variable is Species = Setosa, etc. If i.equal = ": " the display becomes Species: Setosa.

depvar

Logical, default is TRUE. Whether a first line containing the dependent variables should be shown.

style.tex

An object created by the function style.tex. It represents the style of the Latex table, see the documentation of style.tex.

style.df

An object created by the function style.df. It represents the style of the data frame returned (if tex = FALSE), see the documentation of style.df.

notes

(Tex only.) Character vector. If provided, a "notes" section will be added at the end right after the end of the table, containing the text of this argument. Note that if it is a vector, it will be collapsed with new lines.

group

A list. The list elements should be vectors of regular expressions. For each elements of this list: A new line in the table is created, all variables that are matched by the regular expressions are discarded (same effect as the argument drop) and TRUE or FALSE will appear in the model cell, depending on whether some of the previous variables were found in the model. Example: group=list("Controls: personal traits"=c("gender", "height", "weight")) will create an new line with "Controls: personal traits" in the leftmost cell, all three variables gender, height and weight are discarded, TRUE appearing in each model containing at least one of the three variables (the style of TRUE/FALSE is governed by the argument yesNo). You can control the placement of the new row by using 1 or 2 special characters at the start of the row name. The meaning of these special characters are: 1) "^": coef., "-": fixed-effect, "_": stats, section; 2) "^": 1st, "_": last, row. For example: group=list("_^Controls"=stuff) will place the line at the top of the 'stats' section, and using group=list("^_Controls"=stuff) will make the row appear at the bottom of the coefficients section. For details, see the dedicated section.

extraline

A vector, a list or a one sided formula. The list elements should be either a vector representing the value of each cell, a list of the form list("item1" = #item1, "item2" = #item2, etc), or a function. This argument can be many things, please have a look at the dedicated help section; a simplified description follows. For each elements of this list: A new line in the table is created, the list name being the row name and the vector being the content of the cells. Example: extraline=list("Sub-sample"=c("<20 yo", "all", ">50 yo")) will create an new line with "Sub-sample" in the leftmost cell, the vector filling the content of the cells for the three models. You can control the placement of the new row by using 1 or 2 special characters at the start of the row name. The meaning of these special characters are: 1) "^": coef., "-": fixed-effect, "_": stats, section; 2) "^": 1st, "_": last, row. For example: extraline=list("__Controls"=stuff) will place the line at the bottom of the stats section, and using extraline=list("^^Controls"=stuff) will make the row appear at the top of the 'coefficients' section. For details, see the dedicated section.

fixef.group

Logical scalar or list (default is NULL). If equal to TRUE, then all fixed-effects always appearing jointly in models will be grouped in one row. If a list, its elements must be character vectors of regular expressions and the list names will be the row names. For ex. fixef.group=list("Dates fixed-effects"="Month|Day") will remove the "Month" and "Day" fixed effects from the display and replace them with a single row named "Dates fixed-effects". You can monitor the placement of the new row with the special characters telling where to place the row within a section: "^" (first), or "_" (last); and in which section it should appear: "^" (coef.), "-" (fixed-effects), or "_" (stat.). These two special characters must appear first in the row names. Please see the dedicated section

placement

(Tex only.) Character string giving the position of the float in Latex. Default is "htbp". It must consist of only the characters 'h', 't', 'b', 'p', 'H' and '!'. Reminder: h: here; t: top; b: bottom; p: float page; H: definitely here; !: prevents Latex to look for other positions. Note that it can be equal to the empty string (and you'll get the default placement).

drop.section

Character vector which can be of length 0 (i.e. equal to NULL). Can contain the values "fixef", "slopes" or "stats". It would drop, respectively, the fixed-effects section, the variables with varying slopes section or the fit statistics section.

poly_dict

Character vector, default is c("", " square", " cube"). When raw polynomials (x^2, etc) are used, the variables are automatically renamed and poly_dict rules the display of the power. For powers greater than the number of elements of the vector, the value displayed is $^{pow}$ in Latex and ^ pow in the R console.

postprocess.tex

A function that will postprocess the character vector defining the latex table. Only when tex = TRUE. By default it is equal to NULL, meaning that there is no postprocessing. When tex = FALSE, see the argument postprocess.df. See details.

postprocess.df

A function that will postprocess.tex the resulting data.frame. Only when tex = FALSE. By default it is equal to NULL, meaning that there is no postprocessing. When tex = TRUE, see the argument postprocess.tex.

fit_format

Character scalar, default is "__var__". Only used in the presence of IVs. By default the endogenous regressors are named fit_varname in the second stage. The format of the endogenous regressor to appear in the table is governed by fit_format. For instance, by default, the prefix "fit_" is removed, leading to only varname to appear. If fit_format = "$\\hat{__var__}$", then "$\hat{varname}$" will appear in the table.

coef.just

(DF only.) Either ".", "(", "l", "c" or "r", default is NULL. How the coefficients should be justified. If NULL then they are right aligned if se.below = FALSE and aligned to the dot if se.below = TRUE. The keywords stand respectively for dot-, parenthesis-, left-, center- and right-aligned.

meta

(Tex only.) A one-sided formula that shall contain the following elements: date or time, sys, author, comment and call. Default is NULL. This argument is a shortcut to controlling the meta information that can be displayed in comments before the table. Typically if the element is in the formula, it means that the argument will be equal to TRUE. Example: meta = ~time+call is equivalent to meta.time = TRUE and meta.call = TRUE. The "author" and "comment" elements are a bit special. Using meta = ~author("Mark") is equivalent to meta.author = "Mark" while meta=~author is equiv. to meta.author = TRUE. The "comment" must be used with a character string inside: meta = ~comment("this is a comment"). The order in the formula controls the order of appearance of the meta elements. It also has precedence over the meta.XX arguments.

meta.time

(Tex only.) Either a logical scalar (default is FALSE) or "time" or "date". Whether to include the time (if TRUE or "time") or the date (if "date") of creation of the table in a comment right before the table.

meta.author

(Tex only.) A logical scalar (default is FALSE) or a character vector. If TRUE then the identity of the author (deduced from the system user in Sys.info()) is inserted in a comment right before the table. If a character vector, then it should contain author names that will be inserted as comments before the table, prefixed with "Created by:". For free-form comments see the argument meta.comment.

meta.sys

(Tex only.) A logical scalar, default is FALSE. Whether to include system information (from Sys.info()) in a comment right before the table.

meta.call

(Tex only.) Logical scalar, default is FALSE. If TRUE then the call to the function is inserted right before the table in a comment.

meta.comment

(Tex only.) A character vector containing free-form comments to be inserted right before the table.

reset

(setFixest_etable only.) Logical, default is FALSE. If TRUE, this will reset all the default values that were already set by the user in previous calls.

save

Either a logical or equal to "reset". Default is FALSE. If TRUE then the value is set permanently at the project level, this means that if you restart R, you will still obtain the previously saved defaults. This is done by writing in the ".Renviron" file, located in the project's working directory, hence we must have write permission there for this to work. If equal to "reset", the default at the project level is erased.

Value

If tex = TRUE, the lines composing the Latex table are returned invisibly while the table is directly prompted on the console.

If tex = FALSE, the data.frame is directly returned. If the argument file is not missing, the data.frame is printed and returned invisibly.

Details

The function esttex is equivalent to the function etable with argument tex = TRUE.

The function esttable is equivalent to the function etable with argument tex = FALSE.

To display the table, you will need the Latex package booktabs which contains the \toprule, \midrule and \bottomrule commands.

You can permanently change the way your table looks in Latex by using setFixest_etable. The following vignette gives an example as well as illustrates how to use the style and postprocessing functions: Exporting estimation tables.

When the argument postprocessing.tex is not missing, two additional tags will be included in the character vector returned by etable: "%start:tab\n" and "%end:tab\n". These can be used to identify the start and end of the tabular and are useful to insert code within the table environment.

Functions

  • esttable: Exports the results of multiple fixest estimations in a Latex table.

  • esttex: Exports the results of multiple fixest estimations in a Latex table.

How does digits handle the number of decimals displayed?

The default display of decimals is the outcome of an algorithm. Let's take the example of digits = 3 which "kind of" requires 3 significant digits to be displayed.

For numbers greater than 1 (in absolute terms), their integral part is always displayed and the number of decimals shown is equal to digits minus the number of digits in the integral part. This means that 12.345 will be displayed as 12.3. If the number of decimals should be 0, then a single decimal is displayed to suggest that the number is not whole. This means that 1234.56 will be displayed as 1234.5. Note that if the number is whole, no decimals are shown.

For numbers lower than 1 (in absolute terms), the number of decimals displayed is equal to digits except if there are only 0s in which case the first significant digit is shown. This means that 0.01234 will be displayed as 0.012 (first rule), and that 0.000123 will be displayed as 0.0001 (second rule).

Arguments keep, drop and order

The arguments keep, drop and order use regular expressions. If you are not aware of regular expressions, I urge you to learn it, since it is an extremely powerful way to manipulate character strings (and it exists across most programming languages).

For example drop = "Wind" would drop any variable whose name contains "Wind". Note that variables such as "Temp:Wind" or "StrongWind" do contain "Wind", so would be dropped. To drop only the variable named "Wind", you need to use drop = "^Wind$" (with "^" meaning beginning, resp. "$" meaning end, of the string => this is the language of regular expressions).

Although you can combine several regular expressions in a single character string using pipes, drop also accepts a vector of regular expressions.

You can use the special character "!" (exclamation mark) to reverse the effect of the regular expression (this feature is specific to this function). For example drop = "!Wind" would drop any variable that does not contain "Wind".

You can use the special character "%" (percentage) to make reference to the original variable name instead of the aliased name. For example, you have a variable named "Month6", and use a dictionary dict = c(Month6="June"). Thus the variable will be displayed as "June". If you want to delete that variable, you can use either drop="June", or drop="%Month6" (which makes reference to its original name).

The argument order takes in a vector of regular expressions, the order will follow the elements of this vector. The vector gives a list of priorities, on the left the elements with highest priority. For example, order = c("Wind", "!Inter", "!Temp") would give highest priorities to the variables containing "Wind" (which would then appear first), second highest priority is the variables not containing "Inter", last, with lowest priority, the variables not containing "Temp". If you had the following variables: (Intercept), Temp:Wind, Wind, Temp you would end up with the following order: Wind, Temp:Wind, Temp, (Intercept).

The argument extraline

The argument extraline adds well... extra lines to the table. It accepts either a list, or a one-sided formula.

For each line, you can define the values taken by each cell using 4 different ways: a) a vector, b) a list, c) a function, and d) a formula.

If a vector, it should represent the values taken by each cell. Note that if the length of the vector is smaller than the number of models, its values are recycled across models, but the length of the vector is required to be a divisor of the number of models.

If a list, it should be of the form list("item1" = #item1, "item2" = #item2, etc). For example list("A"=2, "B"=3) leads to c("A", "A", "B", "B", "B"). Note that if the number of items is 1, you don't need to add = 1. For example list("A"=2, "B") is valid and leads to c("A", "A", "B". As for the vector the values are recycled if necessary.

If a function, it will be applied to each model and should return a scalar (NA values returned are accepted).

If a formula, it must be one-sided and the elements in the formula must represent either extraline macros, either fit statistics (i.e. valid types of the function fitstat). One new line will be added for each element of the formula. To register extraline macros, you must first register them in extraline_register.

Finally, you can combine as many lines as wished by nesting them in a list. The names of the nesting list are the row titles (values in the leftmost cell). For example extraline = list(~r2, Controls = TRUE, Group = list("A"=2, "B")) will add three lines, the titles of which are "R2", "Controls" and "Group".

Controlling the placement of extra lines

The arguments group, extraline and fixef.group allow to add customized lines in the table. They can be defined via a list where the list name will be the row name. By default, the placement of the extra line is right after the coefficients (except for fixef.group, covered in the last paragraph). For instance, group = list("Controls" = "x[[:digit:]]") will create a line right after the coefficients telling which models contain the control variables.

But the placement can be customized. The previous example (of the controls) will be used for illustration (the mechanism for extraline and fixef.group is identical).

The row names accept 2 special characters at the very start. The first character tells in which section the line should appear: it can be equal to "^", "-", or "_", meaning respectively the coefficients, the fixed-effects and the statistics section (which typically appear at the top, mid and bottom of the table). The second one governs the placement of the new line within the section: it can be equal to "^", meaning first line, or "_", meaning last line.

Let's have some examples. Using the previous example, writing "_^Controls" would place the new line at the top of the statistics section. Writing "-_Controls" places it as the last row of the fixed-effects section; "^^Controls" at the top row of the coefficients section; etc...

The second character is optional, the default placement being in the bottom. This means that "_Controls" would place it at the bottom of the statistics section.

The placement in fixef.group is defined similarly, only the default placement is different. Its default placement is at the top of the fixed-effects section.

Escaping special Latex characters

By default on all instances (with the notable exception of the elements of style.tex) special Latex characters are escaped. This means that title="Exports in million $." will be exported as "Exports in million \$.": the dollar sign will be escaped. This is true for the following characters: &, $, %, _, ^ and #.

Note, importantly, that equations are NOT escaped. This means that title="Functional form $a_i \times x^b$, variation in %." will be displayed as: "Functional form $a_i \times x^b$, variation in \%.": only the last percentage will be escaped.

If for some reason you don't want the escaping to take place, the arguments headers and extraline are the only ones allowing that. To disable escaping, add the special token ":tex:" in the row names. Example: in headers=list(":tex:Row title"="weird & & %\n tex stuff\\"), the elements will be displayed verbatim. Of course, since it can easily ruin your table, it is only recommended to super users.

See also

See also the main estimation functions femlm, feols or feglm. Use summary.fixest to see the results with the appropriate standard-errors, fixef.fixest to extract the fixed-effects coefficients.

Author

Laurent Berge

Examples

aq = airquality est1 = feols(Ozone ~ i(Month) / Wind + Temp, data = aq)
#> NOTE: 37 observations removed because of NA values (LHS: 37).
est2 = feols(Ozone ~ i(Month, Wind) + Temp | Month, data = aq)
#> NOTE: 37 observations removed because of NA values (LHS: 37).
# Displaying the two results in a single table etable(est1, est2)
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> (Intercept) -100.2*** (26.35) #> Month = 6 -54.99* (26.34) #> Month = 7 35.89. (18.37) #> Month = 8 44.52* (18.05) #> Month = 9 -11.78 (18.22) #> Temp 2.042*** (0.3078) 2.042*** (0.2242) #> Wind x Month = 5 -1.086 (1.127) -1.086** (0.1408) #> Wind x Month = 6 2.046 (1.784) 2.046*** (0.0398) #> Wind x Month = 7 -5.616*** (1.316) -5.616*** (0.1554) #> Wind x Month = 8 -6.515*** (1.220) -6.515*** (0.2203) #> Wind x Month = 9 -1.349 (1.147) -1.349* (0.3175) #> Fixed-Effects: ----------------- ------------------ #> Month No Yes #> ________________ _________________ __________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# keep/drop: keeping only interactions etable(est1, est2, keep = " x ")
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> Wind x Month = 5 -1.086 (1.127) -1.086** (0.1408) #> Wind x Month = 6 2.046 (1.784) 2.046*** (0.0398) #> Wind x Month = 7 -5.616*** (1.316) -5.616*** (0.1554) #> Wind x Month = 8 -6.515*** (1.220) -6.515*** (0.2203) #> Wind x Month = 9 -1.349 (1.147) -1.349* (0.3175) #> Fixed-Effects: ----------------- ------------------ #> Month No Yes #> ________________ _________________ __________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# or using drop (see regexp help): etable(est1, est2, drop = "^(Month|Temp|\\()")
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> Wind x Month = 5 -1.086 (1.127) -1.086** (0.1408) #> Wind x Month = 6 2.046 (1.784) 2.046*** (0.0398) #> Wind x Month = 7 -5.616*** (1.316) -5.616*** (0.1554) #> Wind x Month = 8 -6.515*** (1.220) -6.515*** (0.2203) #> Wind x Month = 9 -1.349 (1.147) -1.349* (0.3175) #> Fixed-Effects: ----------------- ------------------ #> Month No Yes #> ________________ _________________ __________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# keep/drop: dropping interactions etable(est1, est2, drop = " x ")
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> (Intercept) -100.2*** (26.35) #> Month = 6 -54.99* (26.34) #> Month = 7 35.89. (18.37) #> Month = 8 44.52* (18.05) #> Month = 9 -11.78 (18.22) #> Temp 2.042*** (0.3078) 2.042*** (0.2242) #> Fixed-Effects: ----------------- ----------------- #> Month No Yes #> _______________ _________________ _________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# or using keep ("!" reverses the effect): etable(est1, est2, keep = "! x ")
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> (Intercept) -100.2*** (26.35) #> Month = 6 -54.99* (26.34) #> Month = 7 35.89. (18.37) #> Month = 8 44.52* (18.05) #> Month = 9 -11.78 (18.22) #> Temp 2.042*** (0.3078) 2.042*** (0.2242) #> Fixed-Effects: ----------------- ----------------- #> Month No Yes #> _______________ _________________ _________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# order: Wind variable first, intercept last (note the "!" to reverse the effect) etable(est1, est2, order = c("Wind", "!Inter"))
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> Wind x Month = 5 -1.086 (1.127) -1.086** (0.1408) #> Wind x Month = 6 2.046 (1.784) 2.046*** (0.0398) #> Wind x Month = 7 -5.616*** (1.316) -5.616*** (0.1554) #> Wind x Month = 8 -6.515*** (1.220) -6.515*** (0.2203) #> Wind x Month = 9 -1.349 (1.147) -1.349* (0.3175) #> Month = 6 -54.99* (26.34) #> Month = 7 35.89. (18.37) #> Month = 8 44.52* (18.05) #> Month = 9 -11.78 (18.22) #> Temp 2.042*** (0.3078) 2.042*** (0.2242) #> (Intercept) -100.2*** (26.35) #> Fixed-Effects: ----------------- ------------------ #> Month No Yes #> ________________ _________________ __________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# Month, then interactions, then the rest etable(est1, est2, order = c("^Month", " x "))
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> Month = 6 -54.99* (26.34) #> Month = 7 35.89. (18.37) #> Month = 8 44.52* (18.05) #> Month = 9 -11.78 (18.22) #> Wind x Month = 5 -1.086 (1.127) -1.086** (0.1408) #> Wind x Month = 6 2.046 (1.784) 2.046*** (0.0398) #> Wind x Month = 7 -5.616*** (1.316) -5.616*** (0.1554) #> Wind x Month = 8 -6.515*** (1.220) -6.515*** (0.2203) #> Wind x Month = 9 -1.349 (1.147) -1.349* (0.3175) #> (Intercept) -100.2*** (26.35) #> Temp 2.042*** (0.3078) 2.042*** (0.2242) #> Fixed-Effects: ----------------- ------------------ #> Month No Yes #> ________________ _________________ __________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# # dict # # You can rename variables with dict = c(var1 = alias1, var2 = alias2, etc) # You can also rename values taken by factors. # Here's a full example: dict = c(Temp = "Temperature", "Month::5"="May", "6"="Jun") etable(est1, est2, dict = dict)
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> (Intercept) -100.2*** (26.35) #> Month = Jun -54.99* (26.34) #> Month = 7 35.89. (18.37) #> Month = 8 44.52* (18.05) #> Month = 9 -11.78 (18.22) #> Temperature 2.042*** (0.3078) 2.042*** (0.2242) #> Wind x May -1.086 (1.127) -1.086** (0.1408) #> Wind x Month = Jun 2.046 (1.784) 2.046*** (0.0398) #> Wind x Month = 7 -5.616*** (1.316) -5.616*** (0.1554) #> Wind x Month = 8 -6.515*** (1.220) -6.515*** (0.2203) #> Wind x Month = 9 -1.349 (1.147) -1.349* (0.3175) #> Fixed-Effects: ----------------- ------------------ #> Month No Yes #> __________________ _________________ __________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# Note the difference of treatment between Jun and May # Assume the following dictionary: dict = c("Month::5"="May", "Month::6"="Jun", "Month::7"="Jul", "Month::8"="Aug", "Month::9"="Sep") # We would like to keep only the Months, but now the names are all changed... # How to do? # We can use the special character '%' to make reference to the original names. etable(est1, est2, dict = dict, keep = "%Month")
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> Jun -54.99* (26.34) #> Jul 35.89. (18.37) #> Aug 44.52* (18.05) #> Sep -11.78 (18.22) #> Wind x May -1.086 (1.127) -1.086** (0.1408) #> Wind x Jun 2.046 (1.784) 2.046*** (0.0398) #> Wind x Jul -5.616*** (1.316) -5.616*** (0.1554) #> Wind x Aug -6.515*** (1.220) -6.515*** (0.2203) #> Wind x Sep -1.349 (1.147) -1.349* (0.3175) #> Fixed-Effects: ----------------- ------------------ #> Month No Yes #> _______________ _________________ __________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# # signifCode # etable(est1, est2, signifCode = c(" A"=0.01, " B"=0.05, " C"=0.1, " D"=0.15, " F"=1))
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> (Intercept) -100.2 A (26.35) #> Month = 6 -54.99 B (26.34) #> Month = 7 35.89 C (18.37) #> Month = 8 44.52 B (18.05) #> Month = 9 -11.78 F (18.22) #> Temp 2.042 A (0.3078) 2.042 A (0.2242) #> Wind x Month = 5 -1.086 F (1.127) -1.086 A (0.1408) #> Wind x Month = 6 2.046 F (1.784) 2.046 A (0.0398) #> Wind x Month = 7 -5.616 A (1.316) -5.616 A (0.1554) #> Wind x Month = 8 -6.515 A (1.220) -6.515 A (0.2203) #> Wind x Month = 9 -1.349 F (1.147) -1.349 B (0.3175) #> Fixed-Effects: ---------------- ----------------- #> Month No Yes #> ________________ ________________ _________________ #> S.E. type IID by: Month #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# # Using the argument style to customize Latex exports # # If you don't like the default layout of the table, no worries! # You can modify many parameters with the argument style # To drop the headers before each section, use: # Note that a space adds an extra line style_noHeaders = style.tex(var.title = "", fixef.title = "", stats.title = " ") etable(est1, est2, dict = dict, tex = TRUE, style.tex = style_noHeaders)
#> \begin{tabular}{lcc} #> \tabularnewline\midrule\midrule #> Dependent Variable: & \multicolumn{2}{c}{Ozone}\\ #> Model: & (1) & (2)\\ #> (Intercept) & -100.2$^{***}$ & \\ #> & (26.35) & \\ #> Jun & -54.99$^{**}$ & \\ #> & (26.34) & \\ #> Jul & 35.89$^{*}$ & \\ #> & (18.37) & \\ #> Aug & 44.52$^{**}$ & \\ #> & (18.05) & \\ #> Sep & -11.78 & \\ #> & (18.22) & \\ #> Temp & 2.042$^{***}$ & 2.042$^{***}$\\ #> & (0.3078) & (0.2242)\\ #> Wind $\times$ May & -1.086 & -1.086$^{***}$\\ #> & (1.127) & (0.1408)\\ #> Wind $\times$ Jun & 2.046 & 2.046$^{***}$\\ #> & (1.784) & (0.0398)\\ #> Wind $\times$ Jul & -5.616$^{***}$ & -5.616$^{***}$\\ #> & (1.316) & (0.1554)\\ #> Wind $\times$ Aug & -6.515$^{***}$ & -6.515$^{***}$\\ #> & (1.220) & (0.2203)\\ #> Wind $\times$ Sep & -1.349 & -1.349$^{**}$\\ #> & (1.147) & (0.3175)\\ #> Month & & Yes\\ #> & & \\ #> Observations & 116 & 116\\ #> R$^2$ & 0.68106 & 0.68106\\ #> Within R$^2$ & & 0.58296\\ #> \midrule\midrule\multicolumn{3}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\ #> \end{tabular}
# To change the lines of the table + dropping the table footer style_lines = style.tex(line.top = "\\toprule", line.bottom = "\\bottomrule", tablefoot = FALSE) etable(est1, est2, dict = dict, tex = TRUE, style.tex = style_lines)
#> \begin{tabular}{lcc} #> \toprule #> Dependent Variable: & \multicolumn{2}{c}{Ozone}\\ #> Model: & (1) & (2)\\ #> \midrule \emph{Variables} & & \\ #> (Intercept) & -100.2$^{***}$ & \\ #> & (26.35) & \\ #> Jun & -54.99$^{**}$ & \\ #> & (26.34) & \\ #> Jul & 35.89$^{*}$ & \\ #> & (18.37) & \\ #> Aug & 44.52$^{**}$ & \\ #> & (18.05) & \\ #> Sep & -11.78 & \\ #> & (18.22) & \\ #> Temp & 2.042$^{***}$ & 2.042$^{***}$\\ #> & (0.3078) & (0.2242)\\ #> Wind $\times$ May & -1.086 & -1.086$^{***}$\\ #> & (1.127) & (0.1408)\\ #> Wind $\times$ Jun & 2.046 & 2.046$^{***}$\\ #> & (1.784) & (0.0398)\\ #> Wind $\times$ Jul & -5.616$^{***}$ & -5.616$^{***}$\\ #> & (1.316) & (0.1554)\\ #> Wind $\times$ Aug & -6.515$^{***}$ & -6.515$^{***}$\\ #> & (1.220) & (0.2203)\\ #> Wind $\times$ Sep & -1.349 & -1.349$^{**}$\\ #> & (1.147) & (0.3175)\\ #> \midrule \emph{Fixed-effects} & & \\ #> Month & & Yes\\ #> \midrule \emph{Fit statistics} & & \\ #> Standard-Errors & IID & Month \\ #> Observations & 116 & 116\\ #> R$^2$ & 0.68106 & 0.68106\\ #> Within R$^2$ & & 0.58296\\ #> \bottomrule #> \end{tabular}
# Or you have the predefined type "aer" etable(est1, est2, dict = dict, tex = TRUE, style.tex = style.tex("aer"))
#> \begin{tabular}{lcc} #> \toprule #> & \multicolumn{2}{c}{Ozone}\\ #> & (1) & (2)\\ #> \midrule (Intercept) & -100.2$^{***}$ & \\ #> & (26.35) & \\ #> Jun & -54.99$^{**}$ & \\ #> & (26.34) & \\ #> Jul & 35.89$^{*}$ & \\ #> & (18.37) & \\ #> Aug & 44.52$^{**}$ & \\ #> & (18.05) & \\ #> Sep & -11.78 & \\ #> & (18.22) & \\ #> Temp & 2.042$^{***}$ & 2.042$^{***}$\\ #> & (0.3078) & (0.2242)\\ #> Wind $\times$ May & -1.086 & -1.086$^{***}$\\ #> & (1.127) & (0.1408)\\ #> Wind $\times$ Jun & 2.046 & 2.046$^{***}$\\ #> & (1.784) & (0.0398)\\ #> Wind $\times$ Jul & -5.616$^{***}$ & -5.616$^{***}$\\ #> & (1.316) & (0.1554)\\ #> Wind $\times$ Aug & -6.515$^{***}$ & -6.515$^{***}$\\ #> & (1.220) & (0.2203)\\ #> Wind $\times$ Sep & -1.349 & -1.349$^{**}$\\ #> & (1.147) & (0.3175)\\ #> & & \\ #> Standard-Errors & IID & Month \\ #> Observations & 116 & 116\\ #> R$^2$ & 0.68106 & 0.68106\\ #> Within R$^2$ & & 0.58296\\ #> & & \\ #> Month fixed effects & & $\checkmark$\\ #> \bottomrule #> \end{tabular}
# # Group and extraline # # Sometimes it's useful to group control variables into a single line # You can achieve that with the group argument setFixest_fml(..ctrl = ~ poly(Wind, 2) + poly(Temp, 2)) est_c0 = feols(Ozone ~ Solar.R, data = aq)
#> NOTE: 42 observations removed because of NA values (LHS: 37, RHS: 7).
est_c1 = feols(Ozone ~ Solar.R + ..ctrl, data = aq)
#> NOTE: 42 observations removed because of NA values (LHS: 37, RHS: 7).
est_c2 = feols(Ozone ~ Solar.R + Solar.R^2 + ..ctrl, data = aq)
#> NOTE: 42 observations removed because of NA values (LHS: 37, RHS: 7).
etable(est_c0, est_c1, est_c2, group = list(Controls = "poly"))
#> est_c0 est_c1 est_c2 #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) 18.60** (6.748) 29.83*** (4.101) 25.79*** (6.506) #> Solar.R 0.1272*** (0.0328) 0.0659** (0.0201) 0.1347 (0.0881) #> Solar.R square -0.0002 (0.0003) #> Controls No Yes Yes #> _______________ __________________ _________________ ________________ #> S.E. type IID IID IID #> Observations 111 111 111 #> R2 0.12134 0.71231 0.71408 #> Adj. R2 0.11328 0.69861 0.69758
# 'group' here does the same as drop = "poly", but adds an extra line # with TRUE/FALSE where the variables were found # 'extraline' adds an extra line, where you can add the value for each model est_all = feols(Ozone ~ Solar.R + Temp + Wind, data = aq)
#> NOTE: 42 observations removed because of NA values (LHS: 37, RHS: 7).
est_sub1 = feols(Ozone ~ Solar.R + Temp + Wind, data = aq[aq$Month %in% 5:6, ])
#> NOTE: 28 observations removed because of NA values (LHS: 26, RHS: 4).
est_sub2 = feols(Ozone ~ Solar.R + Temp + Wind, data = aq[aq$Month %in% 7:8, ])
#> NOTE: 13 observations removed because of NA values (LHS: 10, RHS: 3).
est_sub3 = feols(Ozone ~ Solar.R + Temp + Wind, data = aq[aq$Month == 9, ])
#> NOTE: 1 observation removed because of NA values (LHS: 1).
etable(est_all, est_sub1, est_sub2, est_sub3, extraline = list("Sub-sample" = c("All", "May-June", "Jul.-Aug.", "Sept.")))
#> est_all est_sub1 est_sub2 #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) -64.34** (23.05) -48.52 (30.42) -80.06 (57.59) #> Solar.R 0.0598* (0.0232) 0.0249 (0.0319) 0.0993* (0.0414) #> Temp 1.652*** (0.2535) 1.184** (0.4123) 2.037** (0.6508) #> Wind -3.334*** (0.6544) -1.107 (0.9236) -5.830*** (1.124) #> Sub-sample All May-June Jul.-Aug. #> _______________ __________________ ________________ _________________ #> S.E. type IID IID IID #> Observations 111 33 49 #> R2 0.60589 0.35006 0.66607 #> Adj. R2 0.59484 0.28282 0.64381 #> est_sub3 #> Dependent Var.: Ozone #> #> (Intercept) -112.9** (34.81) #> Solar.R 0.0225 (0.0322) #> Temp 2.005*** (0.3716) #> Wind -1.353 (0.9065) #> Sub-sample Sept. #> _______________ _________________ #> S.E. type IID #> Observations 29 #> R2 0.71724 #> Adj. R2 0.68330
# You can monitor the placement of the new lines with two special characters # at the beginning of the row name. # 1) "^", "-" or "_" which mean the coefficients, the fixed-effects or the # statistics section. # 2) "^" or "_" which mean first or last line of the section # # Ex: starting with "_^" will place the line at the top of the stat. section # starting with "-_" will place the line at the bottom of the FEs section # etc. # # You can use a single character which will represent the section, # the line would then appear at the bottom of the section. # Examples etable(est_c0, est_c1, est_c2, group = list("_Controls" = "poly"))
#> est_c0 est_c1 est_c2 #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) 18.60** (6.748) 29.83*** (4.101) 25.79*** (6.506) #> Solar.R 0.1272*** (0.0328) 0.0659** (0.0201) 0.1347 (0.0881) #> Solar.R square -0.0002 (0.0003) #> _______________ __________________ _________________ ________________ #> S.E. type IID IID IID #> Observations 111 111 111 #> R2 0.12134 0.71231 0.71408 #> Adj. R2 0.11328 0.69861 0.69758 #> Controls No Yes Yes
etable(est_all, est_sub1, est_sub2, est_sub3, extraline = list("^^Sub-sample" = c("All", "May-June", "Jul.-Aug.", "Sept.")))
#> est_all est_sub1 est_sub2 #> Dependent Var.: Ozone Ozone Ozone #> #> Sub-sample All May-June Jul.-Aug. #> (Intercept) -64.34** (23.05) -48.52 (30.42) -80.06 (57.59) #> Solar.R 0.0598* (0.0232) 0.0249 (0.0319) 0.0993* (0.0414) #> Temp 1.652*** (0.2535) 1.184** (0.4123) 2.037** (0.6508) #> Wind -3.334*** (0.6544) -1.107 (0.9236) -5.830*** (1.124) #> _______________ __________________ ________________ _________________ #> S.E. type IID IID IID #> Observations 111 33 49 #> R2 0.60589 0.35006 0.66607 #> Adj. R2 0.59484 0.28282 0.64381 #> est_sub3 #> Dependent Var.: Ozone #> #> Sub-sample Sept. #> (Intercept) -112.9** (34.81) #> Solar.R 0.0225 (0.0322) #> Temp 2.005*** (0.3716) #> Wind -1.353 (0.9065) #> _______________ _________________ #> S.E. type IID #> Observations 29 #> R2 0.71724 #> Adj. R2 0.68330
# # headers # # You can add header lines with 'headers' # These lines will appear at the top of the table # first, 3 estimations est_header = feols(c(Ozone, Solar.R, Wind) ~ poly(Temp, 2), aq) # header => vector: adds a line w/t title etable(est_header, headers = c("A", "A", "B"))
#> model 1 model 2 model 3 #> A A B #> Dependent Var.: Ozone Solar.R Wind #> #> (Intercept) 42.14*** (2.086) 185.2*** (7.218) 9.958*** (0.2548) #> poly(Temp)1 272.4*** (25.94) 317.2*** (91.89) -19.89*** (3.152) #> poly(Temp)2 102.8*** (27.46) -33.07 (92.49) -0.6379 (3.152) #> _______________ ________________ ________________ _________________ #> S.E. type IID IID IID #> Observations 116 146 153 #> R2 0.54422 0.07691 0.20997 #> Adj. R2 0.53615 0.06400 0.19943
# header => list: identical way to do the previous header # The form is: list(item1 = #item1, item2 = #item2, etc) etable(est_header, headers = list("A" = 2, "B" = 1))
#> model 1 model 2 model 3 #> A A B #> Dependent Var.: Ozone Solar.R Wind #> #> (Intercept) 42.14*** (2.086) 185.2*** (7.218) 9.958*** (0.2548) #> poly(Temp)1 272.4*** (25.94) 317.2*** (91.89) -19.89*** (3.152) #> poly(Temp)2 102.8*** (27.46) -33.07 (92.49) -0.6379 (3.152) #> _______________ ________________ ________________ _________________ #> S.E. type IID IID IID #> Observations 116 146 153 #> R2 0.54422 0.07691 0.20997 #> Adj. R2 0.53615 0.06400 0.19943
# Adding a title + # when an element is to be repeated only once, you can avoid the "= 1": etable(est_header, headers = list(Group = list("A" = 2, "B")))
#> model 1 model 2 model 3 #> Group A A B #> Dependent Var.: Ozone Solar.R Wind #> #> (Intercept) 42.14*** (2.086) 185.2*** (7.218) 9.958*** (0.2548) #> poly(Temp)1 272.4*** (25.94) 317.2*** (91.89) -19.89*** (3.152) #> poly(Temp)2 102.8*** (27.46) -33.07 (92.49) -0.6379 (3.152) #> _______________ ________________ ________________ _________________ #> S.E. type IID IID IID #> Observations 116 146 153 #> R2 0.54422 0.07691 0.20997 #> Adj. R2 0.53615 0.06400 0.19943
# To change the placement, add as first character: # - "^" => top # - "-" => mid (default) # - "_" => bottom # Note that "mid" and "top" are only distinguished when tex = TRUE # Placing the new header line at the bottom etable(est_header, headers = list("_Group" = c("A", "A", "B"), "^Currency" = list("US $" = 2, "CA $" = 1)))
#> model 1 model 2 model 3 #> Currency US $ US $ CA $ #> Dependent Var.: Ozone Solar.R Wind #> Group A A B #> #> (Intercept) 42.14*** (2.086) 185.2*** (7.218) 9.958*** (0.2548) #> poly(Temp)1 272.4*** (25.94) 317.2*** (91.89) -19.89*** (3.152) #> poly(Temp)2 102.8*** (27.46) -33.07 (92.49) -0.6379 (3.152) #> _______________ ________________ ________________ _________________ #> S.E. type IID IID IID #> Observations 116 146 153 #> R2 0.54422 0.07691 0.20997 #> Adj. R2 0.53615 0.06400 0.19943
# In Latex, you can add "grouped underlines" (cmidrule from the booktabs package) # by adding ":_:" in the title: etable(est_header, tex = TRUE, headers = list("^:_:Group" = c("A", "A", "B")))
#> \begin{tabular}{lccc} #> \tabularnewline\midrule\midrule #> Group & \multicolumn{2}{c}{A} & B \\ \cmidrule(lr){2-3} \cmidrule(lr){4-4} #> Dependent Variables: & Ozone & Solar.R & Wind\\ #> Model: & (1) & (2) & (3)\\ #> \midrule \emph{Variables} & & & \\ #> (Intercept) & 42.14$^{***}$ & 185.2$^{***}$ & 9.958$^{***}$\\ #> & (2.086) & (7.218) & (0.2548)\\ #> poly(Temp)1 & 272.4$^{***}$ & 317.2$^{***}$ & -19.89$^{***}$\\ #> & (25.94) & (91.89) & (3.152)\\ #> poly(Temp)2 & 102.8$^{***}$ & -33.07 & -0.6379\\ #> & (27.46) & (92.49) & (3.152)\\ #> \midrule \emph{Fit statistics} & & & \\ #> Observations & 116 & 146 & 153\\ #> R$^2$ & 0.54422 & 0.07691 & 0.20997\\ #> Adjusted R$^2$ & 0.53615 & 0.06400 & 0.19943\\ #> \midrule\midrule\multicolumn{4}{l}{\emph{IID standard-errors in parentheses}}\\ #> \multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\ #> \end{tabular}
# # fixef.group # # You can group the fixed-effects line with fixef.group est_0fe = feols(Ozone ~ Solar.R + Temp + Wind, aq)
#> NOTE: 42 observations removed because of NA values (LHS: 37, RHS: 7).
est_1fe = feols(Ozone ~ Solar.R + Temp + Wind | Month, aq)
#> NOTE: 42 observations removed because of NA values (LHS: 37, RHS: 7).
est_2fe = feols(Ozone ~ Solar.R + Temp + Wind | Month + Day, aq)
#> NOTE: 42 observations removed because of NA values (LHS: 37, RHS: 7).
# A) automatic way => simply use fixef.group = TRUE etable(est_0fe, est_2fe, fixef.group = TRUE)
#> est_0fe est_2fe #> Dependent Var.: Ozone Ozone #> #> (Intercept) -64.34** (23.05) #> Solar.R 0.0598* (0.0232) 0.0509 (0.0428) #> Temp 1.652*** (0.2535) 2.052** (0.2390) #> Wind -3.334*** (0.6544) -3.289* (1.051) #> Fixed-Effects: ------------------ ---------------- #> Month and Day No Yes #> _______________ __________________ ________________ #> S.E. type IID by: Month #> Observations 111 111 #> R2 0.60589 0.81604 #> Within R2 -- 0.61471
# Note that when grouping would lead to inconsistencies across models, # it is avoided etable(est_0fe, est_1fe, est_2fe, fixef.group = TRUE)
#> est_0fe est_1fe est_2fe #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) -64.34** (23.05) #> Solar.R 0.0598* (0.0232) 0.0522 (0.0408) 0.0509 (0.0428) #> Temp 1.652*** (0.2535) 1.875*** (0.1816) 2.052** (0.2390) #> Wind -3.334*** (0.6544) -3.109. (1.306) -3.289* (1.051) #> Fixed-Effects: ------------------ ----------------- ---------------- #> Month No Yes Yes #> Day No No Yes #> _______________ __________________ _________________ ________________ #> S.E. type IID by: Month by: Month #> Observations 111 111 111 #> R2 0.60589 0.63686 0.81604 #> Within R2 -- 0.53154 0.61471
# B) customized way => use a list etable(est_0fe, est_2fe, fixef.group = list("Dates" = "Month|Day"))
#> est_0fe est_2fe #> Dependent Var.: Ozone Ozone #> #> (Intercept) -64.34** (23.05) #> Solar.R 0.0598* (0.0232) 0.0509 (0.0428) #> Temp 1.652*** (0.2535) 2.052** (0.2390) #> Wind -3.334*** (0.6544) -3.289* (1.051) #> Fixed-Effects: ------------------ ---------------- #> Dates No Yes #> _______________ __________________ ________________ #> S.E. type IID by: Month #> Observations 111 111 #> R2 0.60589 0.81604 #> Within R2 -- 0.61471
# Note that when a user grouping would lead to inconsistencies, # the term partial replaces yes/no and the fixed-effects are not removed. etable(est_0fe, est_1fe, est_2fe, fixef.group = list("Dates" = "Month|Day"))
#> Warning: In etable(est_0fe, est_1fe, est_2fe, fixef.group = l...: #> In 'fixef.group', the group leads to an inconsistent row (defined by #> "Month|Day"). #> To create inconsistent rows: use drop.section = 'fixef' combined with #> the arghument 'extraline'.
#> est_0fe est_1fe est_2fe #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) -64.34** (23.05) #> Solar.R 0.0598* (0.0232) 0.0522 (0.0408) 0.0509 (0.0428) #> Temp 1.652*** (0.2535) 1.875*** (0.1816) 2.052** (0.2390) #> Wind -3.334*** (0.6544) -3.109. (1.306) -3.289* (1.051) #> Fixed-Effects: ------------------ ----------------- ---------------- #> Dates No partial Yes #> Month No Yes Yes #> Day No No Yes #> _______________ __________________ _________________ ________________ #> S.E. type IID by: Month by: Month #> Observations 111 111 111 #> R2 0.60589 0.63686 0.81604 #> Within R2 -- 0.53154 0.61471
# Using customized placement => as with 'group' and 'extraline', # the user can control the placement of the new line. # See the previous 'group' examples and the dedicated section in the help. # On top of the coefficients: etable(est_0fe, est_2fe, fixef.group = list("^^Dates" = "Month|Day"))
#> est_0fe est_2fe #> Dependent Var.: Ozone Ozone #> #> Dates No Yes #> (Intercept) -64.34** (23.05) #> Solar.R 0.0598* (0.0232) 0.0509 (0.0428) #> Temp 1.652*** (0.2535) 2.052** (0.2390) #> Wind -3.334*** (0.6544) -3.289* (1.051) #> _______________ __________________ ________________ #> S.E. type IID by: Month #> Observations 111 111 #> R2 0.60589 0.81604 #> Within R2 -- 0.61471
# Last line of the statistics etable(est_0fe, est_2fe, fixef.group = list("_Dates" = "Month|Day"))
#> est_0fe est_2fe #> Dependent Var.: Ozone Ozone #> #> (Intercept) -64.34** (23.05) #> Solar.R 0.0598* (0.0232) 0.0509 (0.0428) #> Temp 1.652*** (0.2535) 2.052** (0.2390) #> Wind -3.334*** (0.6544) -3.289* (1.051) #> _______________ __________________ ________________ #> S.E. type IID by: Month #> Observations 111 111 #> R2 0.60589 0.81604 #> Within R2 -- 0.61471 #> Dates No Yes
# # Using custom functions to compute the standard errors # # You can use external functions to compute the VCOVs # by feeding functions in the 'vcov' argument. # Let's use some covariances from the sandwich package etable(est_c0, est_c1, est_c2, vcov = sandwich::vcovHC)
#> est_c0 est_c1 est_c2 #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) 18.60*** (3.949) 29.83*** (3.064) 25.79*** (4.436) #> Solar.R 0.1272*** (0.0266) 0.0659*** (0.0150) 0.1347* (0.0651) #> poly(Wind)1 -155.5*** (39.20) -153.6*** (39.12) #> poly(Wind)2 98.77** (36.32) 98.88** (36.42) #> poly(Temp)1 166.7*** (25.38) 159.3*** (25.99) #> poly(Temp)2 67.20* (27.38) 68.77* (27.36) #> Solar.R square -0.0002 (0.0002) #> _______________ __________________ __________________ _________________ #> S.E. type vcovHC vcovHC vcovHC #> Observations 111 111 111 #> R2 0.12134 0.71231 0.71408 #> Adj. R2 0.11328 0.69861 0.69758
# To add extra arguments to vcovHC, you need to write your wrapper: etable(est_c0, est_c1, est_c2, vcov = function(x) sandwich::vcovHC(x, type = "HC0"))
#> est_c0 est_c1 est_c2 #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) 18.60*** (3.850) 29.83*** (2.890) 25.79*** (4.091) #> Solar.R 0.1272*** (0.0260) 0.0659*** (0.0140) 0.1347* (0.0603) #> poly(Wind)1 -155.5*** (35.29) -153.6*** (35.11) #> poly(Wind)2 98.77** (32.31) 98.88** (32.35) #> poly(Temp)1 166.7*** (23.41) 159.3*** (23.76) #> poly(Temp)2 67.20** (24.84) 68.77** (24.68) #> Solar.R square -0.0002 (0.0002) #> _______________ __________________ __________________ _________________ #> S.E. type vcovHC(type="HC0") vcovHC(type="HC0") vcovHC(type="HC.. #> Observations 111 111 111 #> R2 0.12134 0.71231 0.71408 #> Adj. R2 0.11328 0.69861 0.69758
# # Customize which fit statistic to display # # You can change the fit statistics with the argument fitstat # and you can rename them with the dictionary etable(est1, est2, fitstat = ~ r2 + n + G)
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> (Intercept) -100.2*** (26.35) #> Month = 6 -54.99* (26.34) #> Month = 7 35.89. (18.37) #> Month = 8 44.52* (18.05) #> Month = 9 -11.78 (18.22) #> Temp 2.042*** (0.3078) 2.042*** (0.2242) #> Wind x Month = 5 -1.086 (1.127) -1.086** (0.1408) #> Wind x Month = 6 2.046 (1.784) 2.046*** (0.0398) #> Wind x Month = 7 -5.616*** (1.316) -5.616*** (0.1554) #> Wind x Month = 8 -6.515*** (1.220) -6.515*** (0.2203) #> Wind x Month = 9 -1.349 (1.147) -1.349* (0.3175) #> Fixed-Effects: ----------------- ------------------ #> Month No Yes #> ________________ _________________ __________________ #> S.E. type IID by: Month #> R2 0.68106 0.68106 #> Observations 116 116 #> G 105 5
# If you use a formula, '.' means the default: etable(est1, est2, fitstat = ~ ll + .)
#> est1 est2 #> Dependent Var.: Ozone Ozone #> #> (Intercept) -100.2*** (26.35) #> Month = 6 -54.99* (26.34) #> Month = 7 35.89. (18.37) #> Month = 8 44.52* (18.05) #> Month = 9 -11.78 (18.22) #> Temp 2.042*** (0.3078) 2.042*** (0.2242) #> Wind x Month = 5 -1.086 (1.127) -1.086** (0.1408) #> Wind x Month = 6 2.046 (1.784) 2.046*** (0.0398) #> Wind x Month = 7 -5.616*** (1.316) -5.616*** (0.1554) #> Wind x Month = 8 -6.515*** (1.220) -6.515*** (0.2203) #> Wind x Month = 9 -1.349 (1.147) -1.349* (0.3175) #> Fixed-Effects: ----------------- ------------------ #> Month No Yes #> ________________ _________________ __________________ #> S.E. type IID by: Month #> Log-Likelihood -503.37 -503.37 #> Observations 116 116 #> R2 0.68106 0.68106 #> Within R2 -- 0.58296
# # Computing a different SE for each model # est = feols(Ozone ~ Solar.R + Wind + Temp, data = aq)
#> NOTE: 42 observations removed because of NA values (LHS: 37, RHS: 7).
# # Method 1: use summary s1 = summary(est, "iid") s2 = summary(est, cluster = ~ Month) s3 = summary(est, cluster = ~ Day) s4 = summary(est, cluster = ~ Day + Month) etable(list(s1, s2, s3, s4))
#> model 1 model 2 model 3 #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) -64.34** (23.05) -64.34* (21.30) -64.34** (20.15) #> Solar.R 0.0598* (0.0232) 0.0598 (0.0335) 0.0598*** (0.0162) #> Wind -3.334*** (0.6544) -3.334* (1.181) -3.334*** (0.8343) #> Temp 1.652*** (0.2535) 1.652*** (0.1583) 1.652*** (0.1927) #> _______________ __________________ _________________ __________________ #> S.E. type IID by: Month by: Day #> Observations 111 111 111 #> R2 0.60589 0.60589 0.60589 #> Adj. R2 0.59484 0.59484 0.59484 #> model 4 #> Dependent Var.: Ozone #> #> (Intercept) -64.34* (19.66) #> Solar.R 0.0598 (0.0314) #> Wind -3.334* (1.135) #> Temp 1.652*** (0.1386) #> _______________ _________________ #> S.E. type by: Day & Month #> Observations 111 #> R2 0.60589 #> Adj. R2 0.59484
# # Method 2: using a list in the argument 'vcov' est_bis = feols(Ozone ~ Solar.R + Wind + Temp | Month, data = aq)
#> NOTE: 42 observations removed because of NA values (LHS: 37, RHS: 7).
etable(est, est_bis, vcov = list("hetero", ~ Month))
#> est est_bis #> Dependent Var.: Ozone Ozone #> #> (Intercept) -64.34** (21.23) #> Solar.R 0.0598** (0.0191) 0.0522 (0.0408) #> Wind -3.334*** (0.8749) -3.109. (1.306) #> Temp 1.652*** (0.2025) 1.875*** (0.1816) #> Fixed-Effects: ------------------ ----------------- #> Month No Yes #> _______________ __________________ _________________ #> S.E. type Heteroskedas.-rob. by: Month #> Observations 111 111 #> R2 0.60589 0.63686 #> Within R2 -- 0.53154
# When you have only one model, this model is replicated # along the elements of the vcov list. etable(est, vcov = list("hetero", ~ Month))
#> est est #> Dependent Var.: Ozone Ozone #> #> (Intercept) -64.34** (21.23) -64.34* (21.30) #> Solar.R 0.0598** (0.0191) 0.0598 (0.0335) #> Wind -3.334*** (0.8749) -3.334* (1.181) #> Temp 1.652*** (0.2025) 1.652*** (0.1583) #> _______________ __________________ _________________ #> S.E. type Heteroskedas.-rob. by: Month #> Observations 111 111 #> R2 0.60589 0.60589 #> Adj. R2 0.59484 0.59484
# # Method 3: Using "each" or "times" in vcov # If the first element of the list in 'vcov' is "each" or "times", # then all models will be replicated and all the VCOVs will be # applied to each model. The order in which they are replicated # are governed by the each/times keywords. # each etable(est, est_bis, vcov = list("each", "iid", ~ Month, ~ Day))
#> est est est #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) -64.34** (23.05) -64.34* (21.30) -64.34** (20.15) #> Solar.R 0.0598* (0.0232) 0.0598 (0.0335) 0.0598*** (0.0162) #> Wind -3.334*** (0.6544) -3.334* (1.181) -3.334*** (0.8343) #> Temp 1.652*** (0.2535) 1.652*** (0.1583) 1.652*** (0.1927) #> Fixed-Effects: ------------------ ----------------- ------------------ #> Month No No No #> _______________ __________________ _________________ __________________ #> S.E. type IID by: Month by: Day #> Observations 111 111 111 #> R2 0.60589 0.60589 0.60589 #> Within R2 -- -- -- #> est_bis est_bis est_bis #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) #> Solar.R 0.0522* (0.0237) 0.0522 (0.0408) 0.0522* (0.0202) #> Wind -3.109*** (0.6601) -3.109. (1.306) -3.109*** (0.7986) #> Temp 1.875*** (0.3407) 1.875*** (0.1816) 1.875*** (0.3671) #> Fixed-Effects: ------------------ ----------------- ------------------ #> Month Yes Yes Yes #> _______________ __________________ _________________ __________________ #> S.E. type IID by: Month by: Day #> Observations 111 111 111 #> R2 0.63686 0.63686 0.63686 #> Within R2 0.53154 0.53154 0.53154
# times etable(est, est_bis, vcov = list("times", "iid", ~ Month, ~ Day))
#> est est_bis est #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) -64.34** (23.05) -64.34* (21.30) #> Solar.R 0.0598* (0.0232) 0.0522* (0.0237) 0.0598 (0.0335) #> Wind -3.334*** (0.6544) -3.109*** (0.6601) -3.334* (1.181) #> Temp 1.652*** (0.2535) 1.875*** (0.3407) 1.652*** (0.1583) #> Fixed-Effects: ------------------ ------------------ ----------------- #> Month No Yes No #> _______________ __________________ __________________ _________________ #> S.E. type IID IID by: Month #> Observations 111 111 111 #> R2 0.60589 0.63686 0.60589 #> Within R2 -- 0.53154 -- #> est_bis est est_bis #> Dependent Var.: Ozone Ozone Ozone #> #> (Intercept) -64.34** (20.15) #> Solar.R 0.0522 (0.0408) 0.0598*** (0.0162) 0.0522* (0.0202) #> Wind -3.109. (1.306) -3.334*** (0.8343) -3.109*** (0.7986) #> Temp 1.875*** (0.1816) 1.652*** (0.1927) 1.875*** (0.3671) #> Fixed-Effects: ----------------- ------------------ ------------------ #> Month Yes No Yes #> _______________ _________________ __________________ __________________ #> S.E. type by: Month by: Day by: Day #> Observations 111 111 111 #> R2 0.63686 0.60589 0.63686 #> Within R2 0.53154 -- 0.53154