fixest (version 0.8.4)

coefplot: Plots confidence intervals and point estimates

Description

This function plots the results of estimations (coefficients and confidence intervals). It is flexible and handles interactions in a special way.

Usage

coefplot(
  object,
  ...,
  style,
  sd,
  ci_low,
  ci_high,
  x,
  x.shift = 0,
  horiz = FALSE,
  dict = getFixest_dict(),
  keep,
  drop,
  order,
  ci.width = "1%",
  ci_level = 0.95,
  add = FALSE,
  pt.pch = 20,
  pt.bg = NULL,
  cex = 1,
  pt.cex = cex,
  col = 1:8,
  pt.col = col,
  ci.col = col,
  lwd = 1,
  pt.lwd = lwd,
  ci.lwd = lwd,
  ci.lty = 1,
  grid = TRUE,
  grid.par = list(lty = 3, col = "gray"),
  zero = TRUE,
  zero.par = list(col = "black", lwd = 1),
  pt.join = FALSE,
  pt.join.par = list(col = pt.col, lwd = lwd),
  ci.join = FALSE,
  ci.join.par = list(lwd = lwd, col = col, lty = 2),
  ci.fill = FALSE,
  ci.fill.par = list(col = "lightgray", alpha = 0.5),
  ref = "auto",
  ref.line = "auto",
  ref.line.par = list(col = "black", lty = 2),
  lab.cex,
  lab.min.cex = 0.85,
  lab.max.mar = 0.25,
  lab.fit = "auto",
  xlim.add,
  ylim.add,
  only.params = FALSE,
  only.inter = TRUE,
  sep,
  as.multiple = FALSE,
  bg,
  group = "auto",
  group.par = list(lwd = 2, line = 3, tcl = 0.75),
  main = "Effect on __depvar__",
  value.lab = "Estimate and __ci__ Conf. Int.",
  ylab = NULL,
  xlab = NULL,
  sub = NULL
)

Setting custom default values

The function coefplot dispose of many arguments to parametrize the plots. Most of these arguments can be set once an for all using the function setFixest_coefplot. See Example 3 below for a demonstration.

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 fonction). 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 elments 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).

See Also

See setFixest_coefplot to set the default values of coefplot, and the estimation functions: e.g. feols, fepois, feglm, fenegbin.

Examples

# NOT RUN {
#
# Example 1: Stacking two sets of results on the same graph
#

# Estimation on Iris data with one fixed-effect (Species)
est = feols(Petal.Length ~ Petal.Width + Sepal.Length +
            Sepal.Width | Species, iris)

# Estimation results with clustered standard-errors
# (the default when fixed-effects are present)
est_clu = summary(est)
# Now with "regular" standard-errors
est_std = summary(est, se = "standard")

# You can plot the two results at once
coefplot(list(est_clu, est_std))


# Alternatively, you can use the argument x.shift
# to do it sequentially:

# First graph with clustered standard-errors
coefplot(est, x.shift = -.2)

# 'x.shift' was used to shift the coefficients on the left.

# Second set of results: this time with
#  standard-errors that are not clustered.
coefplot(est, se = "standard", x.shift = .2,
         add = TRUE, col = 2, ci.lty = 2, pch=15)

 # Note that we used 'se', an argument that will
 #  be passed to summary.fixest

legend("topright", col = 1:2, pch = 20, lwd = 1, lty = 1:2,
       legend = c("Clustered", "Standard"), title = "Standard-Errors")


#
# Example 2: Interactions
#


# Now we estimate and plot the "yearly" treatment effects

data(base_did)
base_inter = base_did

# We interact the variable 'period' with the variable 'treat'
est_did = feols(y ~ x1 + i(treat, period, 5) | id+period, base_inter)

# You could have written the following formula instead:
# y ~ x1 + treat::period(5) | id+period

# In the estimation, the variable treat is interacted
#  with each value of period but 5, set as a reference

# When estimations contain interactions, as before,
#  the default behavior of coefplot changes,
#  it now only plots interactions:
coefplot(est_did)

# We can see that the graph is different from before:
#  - only interactions are shown,
#  - the reference is present,
#  - the estimates are joined.
# => this is fully flexible

coefplot(est_did, ref.line = FALSE, pt.join = FALSE)

# Now to display all coefficients, use 'only.inter'
coefplot(est_did, only.inter = FALSE)

#
# What if the interacted variable is not numeric?

# Let's create a "month" variable
all_months = c("aug", "sept", "oct", "nov", "dec", "jan",
               "feb", "mar", "apr", "may", "jun", "jul")
base_inter$period_month = all_months[base_inter$period]

# The new estimation
est = feols(y ~ x1 + i(treat, period_month, "oct") | id+period, base_inter)
# Since 'period_month' of type character, coefplot sorts it
coefplot(est)

# To respect a plotting order, use a factor
base_inter$month_factor = factor(base_inter$period_month, levels = all_months)
est = feols(y ~ x1 + i(treat, month_factor, "oct") | id+period, base_inter)
coefplot(est)


#
# Example 3: Setting defaults
#

# coefplot has many arguments, which makes it highly flexible.
# If you don't like the default style of coefplot. No worries,
# you can set *your* default by using the function
# setFixest_coefplot()

dict = c("Petal.Length"="Length (Petal)", "Petal.Width"="Width (Petal)",
         "Sepal.Length"="Length (Sepal)", "Sepal.Width"="Width (Sepal)")

setFixest_coefplot(ci.col = 2, pt.col = "darkblue", ci.lwd = 3,
                   pt.cex = 2, pt.pch = 15, ci.width = 0, dict = dict)

est = feols(Petal.Length ~ Petal.Width + Sepal.Length +
                Sepal.Width | Species, iris)

# Tadaaa! (Although the colors could be better)
coefplot(est)

# To reset to the default settings:
setFixest_coefplot(reset = TRUE)
coefplot(est)

#
# Example 4: group + cleaning
#

# You can use the argument group to group variables
# You can further use the special character "^^" to clean
#  the beginning of the coef. name: particularly useful for factors

est = feols(Petal.Length ~ Petal.Width + Sepal.Length +
                Sepal.Width + Species, iris)

# No grouping:
coefplot(est)

# now we group by Sepal and Species
coefplot(est, group = list(Sepal = "Sepal", Species = "Species"))

# now we group + clean the beginning of the names using the special character ^^
coefplot(est, group = list(Sepal = "^^Sepal.", Species = "^^Species"))


# }