DIY Conflict Model

April 23, 2024

Step 1: Get Data

`peacesciencer` package

library(peacesciencer)

conflict_df <- create_stateyears(system = 'gw') |>
  filter(year %in% c(1946:1999)) |>
  add_ucdp_acd(type=c("intrastate"), only_wars = FALSE) |>
  add_democracy() |>
  add_creg_fractionalization() |>
  add_sdp_gdp() |>
  add_rugged_terrain()

glimpse(conflict_df)

Rows: 7,036
Columns: 20
$ gwcode         <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ statename      <chr> "United States of America", "United States of America",…
$ year           <dbl> 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1…
$ ucdpongoing    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ ucdponset      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ maxintensity   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ conflict_ids   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ v2x_polyarchy  <dbl> 0.605, 0.587, 0.599, 0.599, 0.587, 0.602, 0.601, 0.594,…
$ polity2        <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,…
$ xm_qudsest     <dbl> 1.259180, 1.259180, 1.252190, 1.252190, 1.270106, 1.259…
$ ethfrac        <dbl> 0.2226323, 0.2248701, 0.2271561, 0.2294918, 0.2318781, …
$ ethpol         <dbl> 0.4152487, 0.4186156, 0.4220368, 0.4255134, 0.4290458, …
$ relfrac        <dbl> 0.4980802, 0.5009111, 0.5037278, 0.5065309, 0.5093204, …
$ relpol         <dbl> 0.7769888, 0.7770017, 0.7770303, 0.7770729, 0.7771274, …
$ wbgdp2011est   <dbl> 28.539, 28.519, 28.545, 28.534, 28.572, 28.635, 28.669,…
$ wbpopest       <dbl> 18.744, 18.756, 18.781, 18.804, 18.821, 18.832, 18.848,…
$ sdpest         <dbl> 28.478, 28.456, 28.483, 28.469, 28.510, 28.576, 28.611,…
$ wbgdppc2011est <dbl> 9.794, 9.762, 9.764, 9.730, 9.752, 9.803, 9.821, 9.857,…
$ rugged         <dbl> 1.073, 1.073, 1.073, 1.073, 1.073, 1.073, 1.073, 1.073,…
$ newlmtnest     <dbl> 3.214868, 3.214868, 3.214868, 3.214868, 3.214868, 3.214…

Your Turn!

Install/load the peacesciencer package
Visit the package website
Create a conflict dataset based on the Gleditsch-Ward system of state-years
Filter the dataset to include only years between 1946 and 1999
Add UCDP/PRIO Armed Conflict Dataset (ACD) data on intrastate wars
Add sets of variables and glimpse() the dataset after each set
- Democracy variables
- Fractionalization variables
- GDP variables
- Rugged terrain variables
Others?

10:00

Step 2: Approximate F&L

Replicate Fearon and Laitin (2003)

One way to do it:

conflict_model <- glm(ucdponset ~ ethfrac + relfrac + v2x_polyarchy + rugged +
                        wbgdppc2011est + wbpopest,
                  data = conflict_df,
                  family = "binomial")

summary(conflict_model)


Call:
glm(formula = ucdponset ~ ethfrac + relfrac + v2x_polyarchy + 
    rugged + wbgdppc2011est + wbpopest, family = "binomial", 
    data = conflict_df)

Coefficients:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -5.69260    1.40788  -4.043 5.27e-05 ***
ethfrac         0.80005    0.38072   2.101  0.03560 *  
relfrac        -0.39138    0.41673  -0.939  0.34764    
v2x_polyarchy  -0.60161    0.50879  -1.182  0.23704    
rugged          0.06413    0.07603   0.843  0.39897    
wbgdppc2011est -0.37188    0.12059  -3.084  0.00204 ** 
wbpopest        0.29318    0.06735   4.353 1.34e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1182.5  on 6150  degrees of freedom
Residual deviance: 1126.7  on 6144  degrees of freedom
  (885 observations deleted due to missingness)
AIC: 1140.7

Number of Fisher Scoring iterations: 7

Your Turn!

Approximate the Fearon and Laitin (2003) model
Use the glm() function to run the model
Add measures of ethnicity, democracy, terrain, population and wealth
Try replacing the original measures with different ones of the same concept
- What happens?
What else could you add?

10:00

Step 3: Visualize Your Model

So we can use ggplot to make a coefficient plot instead…

Two steps… First, we need a coeficient map:

coef_map <- c("ethfrac" = "Ethnic Frac",  # map coefficients
        "relfrac" = "Religions Frac",     #(change names and order)
        "v2x_polyarchy" = "Polyarchy",
        "rugged" = "Terrain",
        "wbgdppc2011est" = "Per capita GDP",
        "wbpopest" = "Population",
        "(Intercept)" = "Intercept")

Then, we can use modelplot to visualize the model:

library(modelsummary)
library(ggplot2)

modelplot(conflict_model, 
          coef_map = rev(coef_map), # rev() reverses list order
          coef_omit = "Intercept", 
          color = "blue") + # use plus to add customizations like any ggplot object
  geom_vline(xintercept = 0, color = "red", linetype = "dashed", linewidth = .75) + # red 0 line
  labs(
    title = "Figure 1: Predictors of Conflict Onset",
    caption = "See appendix for data sources."
  )

Your Turn!

Create a coefficient map for your model
Use modelplot to visualize your model
Don’t forget to add the title and caption

10:00

Step 4: Exponentiate the Coefficients

Exponentiate the Coefficients

modelplot(conflict_model, 
          exponentiate = TRUE, # exponentiate coefficients
          coef_map = rev(coef_map), # rev() reverses list order
          coef_omit = "Intercept", 
          color = "blue") + # use plus to add customizations like any ggplot object
  geom_vline(xintercept = 1, color = "red", linetype = "dashed", linewidth = .75) + # red 0 line
  labs(
    title = "Figure 1: Predictors of Conflict Onset",
    caption = "See appendix for data sources."
  )

Your Turn!

Exponentiate the coefficients in your model
Do this by setting exponentiate = TRUE in modelplot
Interpret the results

10:00

Step 5: Model Predictions

Using `marginaleffects`

# load the marginaleffects library
library(marginaleffects)

# select some countries for a given year
selected_countries <- conflict_df |>
  filter(
    statename %in% c("United States of America", "Venezuela", "Rwanda"),
    year == 1999)

# calculate margins for the subset
marg_effects <- predictions(conflict_model, newdata = selected_countries)

# tidy the results
tidy(marg_effects) |>
  select(estimate, p.value, conf.low, conf.high, statename)

Using `marginaleffects`

# A tibble: 3 × 5
  estimate  p.value conf.low conf.high statename               
     <dbl>    <dbl>    <dbl>     <dbl> <chr>                   
1   0.0123 2.23e-28  0.00567    0.0264 United States of America
2   0.0140 2.25e-70  0.00879    0.0222 Venezuela               
3   0.0286 8.46e-39  0.0170     0.0477 Rwanda

Your Turn!

Use marginaleffects to calculate the predicted probabilities for a few countries
Interpret the results

10:00

Step 6: Regression Tables

Run Multiple Models

ethnicity <- glm(ucdponset ~ ethfrac + relfrac + wbgdppc2011est + wbpopest, # store each model in an object
                  data = conflict_df,
                  family = "binomial")

democracy <- glm(ucdponset ~ v2x_polyarchy + wbgdppc2011est +  wbpopest,
                  data = conflict_df,
                  family = "binomial")

terrain <- glm(ucdponset ~ rugged + wbgdppc2011est + wbpopest ,
                  data = conflict_df,
                  family = "binomial")

full_model <- glm(ucdponset ~ ethfrac + relfrac + v2x_polyarchy + rugged +
                        wbgdppc2011est + wbpopest,
                  data = conflict_df,
                  family = "binomial")

Prep Data for Display

models <- list("Ethnicity" = ethnicity,  # store list of models in an object
               "Democracy" = democracy, 
               "Terrain" = terrain, 
               "Full Model" = full_model)

coef_map <- c("ethfrac" = "Ethnic Frac",  # map coefficients
        "relfrac" = "Religions Frac",     #(change names and order)
        "v2x_polyarchy" = "Polyarchy",
        "rugged" = "Terrain",
        "wbgdppc2011est" = "Per capita GDP",
        "wbpopest" = "Population",
        "(Intercept)" = "Intercept")

caption = "Table 1: Predictors of Conflict Onset" # store caption
reference = "See appendix for data sources."      # store reference notes

Display the Models

Note that you also need the gt package to display the table in this way…

library(modelsummary)
library(gt)

modelsummary(models,                      # display the table
             stars = TRUE,                # include stars for significance
             gof_map = c("nobs"),         # goodness of fit stats to include   
             coef_map = coef_map,         # coefficient mapping
             title = caption,             # title
             notes = reference, 
             output = "gt")           # source note

Display the Models

Table 1: Predictors of Conflict Onset
	Ethnicity	Democracy	Terrain	Full Model
Ethnic Frac	0.744*			0.800*
	(0.367)			(0.381)
Religions Frac	-0.481			-0.391
	(0.411)			(0.417)
Polyarchy		-0.228		-0.602
		(0.436)		(0.509)
Terrain			0.031	0.064
			(0.076)	(0.076)
Per capita GDP	-0.474***	-0.512***	-0.543***	-0.372**
	(0.104)	(0.108)	(0.092)	(0.121)
Population	0.282***	0.297***	0.299***	0.293***
	(0.067)	(0.051)	(0.050)	(0.067)
Intercept	-4.703***	-4.407***	-4.296***	-5.693***
	(1.327)	(1.205)	(1.143)	(1.408)
Num.Obs.	6364	6772	6840	6151
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
See appendix for data sources.

Your Turn!

Run multiple models
Display the results in a regression table
Try rerunning with exponentiate = TRUE
Try getting predicted probabilities with marginaleffects

10:00

DIY Conflict Model

Step 1: Get Data

peacesciencer package

Your Turn!

Step 2: Approximate F&L

Replicate Fearon and Laitin (2003)

Your Turn!

Step 3: Visualize Your Model

Your Turn!

Step 4: Exponentiate the Coefficients

Exponentiate the Coefficients

Exponentiate the Coefficients

Your Turn!

Step 5: Model Predictions

Using marginaleffects

Using marginaleffects

Your Turn!

Step 6: Regression Tables

Run Multiple Models

Prep Data for Display

Display the Models

Display the Models

Your Turn!

`peacesciencer` package

Using `marginaleffects`

Using `marginaleffects`