This function plots the results of estimations (coefficients and confidence intervals). The function `iplot`

restricts the output to variables created with `i`

, either interactions with factors or raw factors.

coefplot( object, ..., style = NULL, 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 = c(20, 17, 15, 21, 24, 22), 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, 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 ) iplot( object, ..., i.select = 1, style = NULL, 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 = c(20, 17, 15, 21, 24, 22), 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, 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 )

object | Can be either: i) an estimation object (obtained for example from |
---|---|

... | Other arguments to be passed to |

style | A character scalar giving the style of the plot to be used. You can set styles with the function |

sd | The standard errors of the estimates. It may be missing. |

ci_low | If |

ci_high | If |

x | The value of the x-axis. If missing, the names of the argument |

x.shift | Shifts the confidence intervals bars to the left or right, depending on the value of |

horiz | A logical scalar, default is |

dict | A named character vector or a logical scalar. It changes the original variable names to the ones contained in the |

keep | Character vector. This element is used to display only a subset of variables. This should be a vector of regular expressions (see |

drop | Character vector. This element is used if some variables are not to be displayed. This should be a vector of regular expressions (see |

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 |

ci.width | The width of the extremities of the confidence intervals. Default is |

ci_level | Scalar between 0 and 1: the level of the CI. By default it is equal to 0.95. |

add | Default is |

pt.pch | The patch of the coefficient estimates. Default is 1 (circle). |

pt.bg | The background color of the point estimate (when the |

cex | Numeric, default is 1. Expansion factor for the points |

pt.cex | The size of the coefficient estimates. Default is the other argument |

col | The color of the points and the confidence intervals. Default is 1 ("black"). Note that you can set the colors separately for each of them with |

pt.col | The color of the coefficient estimates. Default is equal to the other argument |

ci.col | The color of the confidence intervals. Default is equal to the other argument |

lwd | General line with. Default is 1. |

pt.lwd | The line width of the coefficient estimates. Default is equal to the other argument |

ci.lwd | The line width of the confidence intervals. Default is equal to the other argument |

ci.lty | The line type of the confidence intervals. Default is 1. |

grid | Logical, default is |

grid.par | List. Parameters of the grid. The default values are: |

zero | Logical, default is |

zero.par | List. Parameters of the zero-line. The default values are |

pt.join | Logical, default is |

pt.join.par | List. Parameters of the line joining the coefficients. The default values are: |

ci.join | Logical default to |

ci.join.par | A list of parameters to be passed to |

ci.fill | Logical default to |

ci.fill.par | A list of parameters to be passed to |

ref | Used to add points equal to 0 (typically to visualize reference points). Either: i) "auto" (default), ii) a character vector of length 1, iii) a list of length 1, iv) a named integer vector of length 1, or v) a numeric vector. By default, in |

ref.line | Logical or numeric, default is "auto", whose behavior depends on the situation. It is |

ref.line.par | List. Parameters of the vertical line on the reference. The default values are: |

lab.cex | The size of the labels of the coefficients. Default is missing. It is automatically set by an internal algorithm which can go as low as |

lab.min.cex | The minimum size of the coefficients labels, as set by the internal algorithm. Default is 0.85. |

lab.max.mar | The maximum size the left margin can take when trying to fit the coefficient labels into it (only when |

lab.fit | The method to fit the coefficient labels into the plotting region (only when |

xlim.add | A numeric vector of length 1 or 2. It represents an extension factor of xlim, in percentage. Eg: |

ylim.add | A numeric vector of length 1 or 2. It represents an extension factor of ylim, in percentage. Eg: |

only.params | Logical, default is |

sep | The distance between two estimates -- only when argument |

as.multiple | Logical: default is |

bg | Background color for the plot. By default it is white. |

group | A list, default is missing. Each element of the list reports the coefficients to be grouped while the name of the element is the group name. Each element of the list can be either: i) a character vector of length 1, ii) of length 2, or ii) a numeric vector. If equal to: i) then it is interpreted as a pattern: all element fitting the regular expression will be grouped (note that you can use the special character "^^" to clean the beginning of the names, see example), if ii) it corrsponds to the first and last elements to be grouped, if iii) it corresponds to the coefficients numbers to be grouped. If equal to a character vector, you can use a percentage to tell the algorithm to look at the coefficients before aliasing (e.g. |

group.par | A list of parameters controlling the display of the group. The parameters controlling the line are: |

main | The title of the plot. Default is |

value.lab | The label to appear on the side of the coefficient values. If |

ylab | The label of the y-axis, default is |

xlab | The label of the x-axis, default is |

sub | A subtitle, default is |

i.select | Integer scalar, default is 1. In |

`iplot`

: Plots the coefficients generated with i()

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.

The function `iplot`

restricts `coefplot`

to interactions or factors created with the function `i`

. Only *one* of the i-variables will be plotted at a time. If you have several i-variables, you can navigate through them with the `i.select`

argument.

The argument `i.select`

is an index that will go through all the i-variables. It will work well if the variables are pure, meaning not interacted with other variables. If the i-variables are interacted, the index may have an odd behavior but will (in most cases) work all the same, just try some numbers up until you (hopefully) obtain the graph you want.

Note, importantly, that interactions of two factor variables are (in general) disregarded since they would require a 3-D plot to be properly represented.

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).

See `setFixest_coefplot`

to set the default values of `coefplot`

, and the estimation functions: e.g. `feols`

, `fepois`

, `feglm`

, `fenegbin`

.

Laurent Berge

# # 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 = "iid") # 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 = "iid", 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", "IID"), 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(period, treat, 5) | id+period, base_inter) # In the estimation, the variable treat is interacted # with each value of period but 5, set as a reference # coefplot will show all the coefficients: coefplot(est_did)# Note that the grouping of the coefficients is due to 'group = "auto"' # If you want to keep only the coefficients # created with i() (ie the interactions), use iplot iplot(est_did)# 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, # => this is fully flexible iplot(est_did, ref.line = FALSE, pt.join = TRUE)# # 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(period_month, treat, "oct") | id+period, base_inter) # Since 'period_month' of type character, coefplot sorts it iplot(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(month_factor, treat, "oct") | id+period, base_inter) iplot(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 + i(Species), iris) # And that's it coefplot(est)# You can set separate default values for iplot setFixest_coefplot("iplot", pt.join = TRUE, pt.join.par = list(lwd = 2, lty = 2)) iplot(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"))