Set of functions to directly extract some commonly used statistics, like the p-value or
the table of coefficients, from estimations. This was first implemented for
`fixest`

estimations, but has some support for other models.

## Usage

```
# S3 method for fixest
coeftable(
object,
vcov = NULL,
ssc = NULL,
cluster = NULL,
keep = NULL,
drop = NULL,
order = NULL,
list = FALSE,
...
)
# S3 method for fixest
se(
object,
vcov = NULL,
ssc = NULL,
cluster = NULL,
keep = NULL,
drop = NULL,
order = NULL,
...
)
# S3 method for fixest
tstat(
object,
vcov = NULL,
ssc = NULL,
cluster = NULL,
keep = NULL,
drop = NULL,
order = NULL,
...
)
# S3 method for fixest
pvalue(
object,
vcov = NULL,
ssc = NULL,
cluster = NULL,
keep = NULL,
drop = NULL,
order = NULL,
...
)
```

## Arguments

- object
A

`fixest`

object. For example an estimation obtained from`feols`

.- 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.- 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 = "min"`

,`t.df = "min"`

,`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 clusters in the estimation, you could further use`cluster = 1:2`

or leave it blank with`se = "twoway"`

(assuming`var1`

[resp.`var2`

] was the 1st [resp. 2nd] cluster).- keep
Character vector. This element is used to display only a subset of variables. This should be a vector of regular expressions (see

`base::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

`base::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

`base::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.- list
Logical, default is

`FALSE`

. If`TRUE`

, then a nested list is returned, the first layer is accessed with the coefficients names; the second layer with the following values:`coef`

,`se`

,`tstat`

,`pvalue`

. Note that the variable`"(Intercept)"`

is renamed into`"constant"`

.- ...
Other arguments to be passed to

`summary.fixest`

.

## Value

Returns a table of coefficients, with in rows the variables and four columns: the estimate, the standard-error, the t-statistic and the p-value.

If `list = TRUE`

then a nested list is returned, the first layer is accessed with
the coefficients names; the second layer with the following values:
`coef`

, `se`

, `tstat`

, `pvalue`

. For example, with `res = coeftable(est, list = TRUE)`

you can access the SE of the coefficient `x1`

with `res$x1$se`

; and its
coefficient with `res$x1$coef`

, etc.

## Functions

`se(fixest)`

: Extracts the standard-error of an estimation`tstat(fixest)`

: Extracts the t-statistics of an estimation`pvalue(fixest)`

: Extracts the p-value of an estimation

## Examples

```
# Some data and estimation
data(trade)
est = fepois(Euros ~ log(dist_km) | Origin^Product + Year, trade)
#
# Coeftable/se/tstat/pvalue
#
# Default is clustering along Origin^Product
coeftable(est)
#> Estimate Std. Error z value Pr(>|z|)
#> log(dist_km) -1.023957 0.04728994 -21.65275 5.725404e-104
#> attr(,"type")
#> [1] "Clustered (Origin^Product)"
se(est)
#> log(dist_km)
#> 0.04728994
tstat(est)
#> log(dist_km)
#> -21.65275
pvalue(est)
#> log(dist_km)
#> 5.725404e-104
# Now with two-way clustered standard-errors
# and using coeftable()
coeftable(est, cluster = ~Origin + Product)
#> Estimate Std. Error z value Pr(>|z|)
#> log(dist_km) -1.023957 0.0906375 -11.29728 1.35342e-29
#> attr(,"type")
#> [1] "Clustered (Origin & Product)"
se(est, cluster = ~Origin + Product)
#> log(dist_km)
#> 0.0906375
pvalue(est, cluster = ~Origin + Product)
#> log(dist_km)
#> 1.35342e-29
tstat(est, cluster = ~Origin + Product)
#> log(dist_km)
#> -11.29728
# Or you can cluster only once:
est_sum = summary(est, cluster = ~Origin + Product)
coeftable(est_sum)
#> Estimate Std. Error z value Pr(>|z|)
#> log(dist_km) -1.023957 0.0906375 -11.29728 1.35342e-29
#> attr(,"type")
#> [1] "Clustered (Origin & Product)"
se(est_sum)
#> log(dist_km)
#> 0.0906375
tstat(est_sum)
#> log(dist_km)
#> -11.29728
pvalue(est_sum)
#> log(dist_km)
#> 1.35342e-29
# You can use the arguments keep, drop, order
# to rearrange the results
base = iris
names(base) = c("y", "x1", "x2", "x3", "species")
est_iv = feols(y ~ x1 | x2 ~ x3, base)
tstat(est_iv, keep = "x1")
#> x1
#> 7.960735
coeftable(est_iv, keep = "x1|Int")
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 2.438955 0.25349903 9.621160 2.688392e-17
#> x1 0.559183 0.07024264 7.960735 4.261663e-13
coeftable(est_iv, order = "!Int")
#> Estimate Std. Error t value Pr(>|t|)
#> fit_x2 0.4509765 0.01794806 25.126759 4.556383e-55
#> x1 0.5591830 0.07024264 7.960735 4.261663e-13
#> (Intercept) 2.4389548 0.25349903 9.621160 2.688392e-17
#
# Using lists
#
# Returning the coefficients table as a list can be useful for quick
# reference in markdown documents.
# Note that the "(Intercept)" is renamed into "constant"
res = coeftable(est_iv, list = TRUE)
# coefficient of the constant:
res$constant$coef
#> Estimate
#> 2.438955
# pvalue of x1
res$x1$pvalue
#> Pr(>|t|)
#> 4.261663e-13
```