This is a super simple package to help make scatter plots of two
variables after residualizing by covariates. This package uses
fixest
so things are super fast. This is meant to (as much
as possible) be a drop in replacement for fixest::feols
.
You should be able to replace feols
with
fwl_plot
and get a plot.
You can install the development version of fwlplot like so:
::install_github("kylebutts/fwlplot") devtools
Here’s a simple example with fixed effects removed by
fixest
.
library(fwlplot)
library(fixest)
library(data.table)
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.3.1
theme_set(theme_light(base_size = 16))
<- data.table::fread("https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv")
flights $long_distance = (flights$distance > 2000)
flights# Sample 10000 rows
= flights[sample(nrow(flights), 10000), ]
sample
# Without covariates = scatterplot
fwl_plot(dep_delay ~ air_time, data = sample)
# With covaraites = FWL'd scatterplot
fwl_plot(
~ air_time | origin + dest,
dep_delay data = sample, vcov = "hc1"
)
If you have a large dataset, we can plot a sample of points with the
n_sample
argument. This determines the number of points
per plot (see multiple estimation below).
fwl_plot(
~ air_time | origin + dest,
dep_delay # Full dataset for estimation, 1000 obs. for plotting
data = flights, n_sample = 1000
)
feols
compatabilityThis is meant to be a 1:1 drop-in replacement with fixest, so
everything should work by just replacing feols
with
feols(
~ air_time | origin + dest,
dep_delay data = sample, subset = ~long_distance, cluster = ~origin
)#> OLS estimation, Dep. Var.: dep_delay
#> Observations: 1,742
#> Subset: long_distance
#> Fixed-effects: origin: 2, dest: 16
#> Standard-errors: Clustered (origin)
#> Estimate Std. Error t value Pr(>|t|)
#> air_time 0.093106 0.092359 1.00808 0.49744
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 35.2 Adj. R2: 0.003127
#> Within R2: 0.00175
fwl_plot(
~ air_time | origin + dest,
dep_delay data = sample, subset = ~long_distance, cluster = ~origin
)
# Multiple y variables
fwl_plot(
c(dep_delay, arr_delay) ~ air_time | origin + dest,
data = sample
)
# `split` sample
fwl_plot(
c(dep_delay, arr_delay) ~ air_time | origin + dest,
data = sample, split = ~long_distance
)
# `fsplit` = `split` sample and Full sample
fwl_plot(
c(dep_delay, arr_delay) ~ air_time | origin + dest,
data = sample, fsplit = ~long_distance
)