Coefficient plot over multiple models

Question

I have a series of 33 fixed effects regressions. Each regression has its own unique dependent variable: the cost of different trade product types. Each trade product type has two regressions for two different key independent variables, STC_exp and STC_import.

lval28_exports_tradeonly <- felm(lval28 ~ STC_exp | pair + year | 0 | pair, subset(STC_Data, NoTrade28 == 1))
lval28_imports_tradeonly <- felm(lval28 ~ STC_imp | pair + year | 0 | pair, subset(STC_Data, NoTrade28 == 1))

lval29_exports_tradeonly <- felm(lval29 ~ STC_exp | pair + year | 0 | pair, subset(STC_Data, NoTrade29 == 1))
lval29_imports_tradeonly <- felm(lval29 ~ STC_imp | pair + year | 0 | pair, subset(STC_Data, NoTrade29 == 1))

What I want to do is create a coefficient plot so that the two independent variables for each of the dependent variables either share the same line on the coefficient plot or can be grouped together. I tried doing this with plot_summs in jtools package and some other packages, but I'm not having much success.

I can make each figure individually:

ore <- plot_summs(lval26_imports_tradeonly, lval26_exports_tradeonly, coefs = c("Ore" = "STC_exp", "Ore" = "STC_imp"), model.names = c("STC on importer", "STC on exporter"))

inorganic.chemicals <- plot_summs(lval28_imports_tradeonly, lval28_exports_tradeonly, coefs = c("Inorganic Chemicals" = "STC_exp", "Inorganic Chemicals" = "STC_imp"), model.names = c("STC on importer", "STC on exporter"))

But I would like to be able to combine them in some way. Perhaps the jtools package isn't the right away to go?

Answer 1

You could just make it with ggplot directly.

Below, I make some example data that looks to have similar properties to yours. Without your data, I can't replicate your example directly.

library(tibble)
library(dplyr)
library(tidyr)

set.seed(25443)
dat <- tibble(STC_exp  = runif(500, -3, 3), 
              STC_imp = runif(500, -3, 3))

b1 <- runif(33, .1, .5)
b2 <- runif(33, .1, .5)
for(i in 1:33){
  dat[[paste0("lval", i)]] <- b1[i] * dat$STC_exp + b2[i] * dat$STC_imp + rnorm(500, 0, .25)
}

reshape the data to long-format on both STC_ variables and all of the lval variables.

library(tidyr)
dat <- dat %>% pivot_longer(cols=c("STC_exp", "STC_imp"), names_to= "ie", values_to = "stc")
dat <- dat %>% pivot_longer(cols=starts_with("lval"), names_to="var", values_to = "lval")

Run the models and collect the output.

library(purrr)
library(broom)

mods <- dat %>%
  group_by(ie, var) %>% 
  summarise(lm_mod= list(lm(lval ~ stc))) %>%
  mutate(tidied = map(lm_mod,tidy,conf.int = TRUE)) %>%
  unnest(tidied)
mods <- select(mods, -lm_mod) %>% 
  filter(term == "stc")

Make the plot

library(ggplot2)
mods %>% 
  mutate(ie = factor(ie, levels=c("STC_exp", "STC_imp"), 
                     labels=c("STC on Exporter", "STC on Importer"))) %>% 
ggplot(aes(x=estimate, y=var, colour=ie)) + 
  geom_point(position = position_dodge(width=.75)) + 
  geom_errorbarh(aes(xmin=conf.low, xmax=conf.high), position=position_dodge(width=.75), height=0) + 
  labs(x="Estimate", y="", colour="Model") + 
  theme_bw()