Regression Tables

How to Interpret and Create

March 24, 2026

Multiple Logistic Regression

Multiple Logistic Regression


  • Just as with linear models, we can also run multiple logistic regressions
  • We can include multiple predictors in the model
  • Usually we want to include our main predictor of interest and control variables
  • The interpetation of the coefficients is the same as in the bivariate models


Download the data using the peacesciencer package if you haven’t already…


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()


Example, including multiple predictors associated with conflict:

conflict_model <- logistic_reg() |>
  set_engine("glm") |>
  fit(factor(ucdponset) ~ ethfrac + relfrac + v2x_polyarchy + 
                          rugged + wbgdppc2011est + wbpopest,
                  data= conflict_df,
                  family = "binomial")

tidy(conflict_model)
# A tibble: 7 × 5
  term           estimate std.error statistic   p.value
  <chr>             <dbl>     <dbl>     <dbl>     <dbl>
1 (Intercept)     -5.38      1.41      -3.81  0.000138 
2 ethfrac          0.709     0.372      1.91  0.0566   
3 relfrac         -0.288     0.409     -0.702 0.482    
4 v2x_polyarchy   -0.652     0.513     -1.27  0.204    
5 rugged           0.0836    0.0756     1.11  0.269    
6 wbgdppc2011est  -0.392     0.120     -3.26  0.00110  
7 wbpopest         0.285     0.0677     4.20  0.0000263

Predicted Probabilities


Show the code 
# load the 
library(marginaleffects)

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

# extract the model
conflict_fit <- conflict_model$fit

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

# tidy the results
tidy(marg_effects) |>
  select(estimate, p.value, conf.low, conf.high, gw_name)
# A tibble: 3 × 5
  estimate  p.value conf.low conf.high gw_name                 
     <dbl>    <dbl>    <dbl>     <dbl> <chr>                   
1   0.0115 1.63e-28  0.00527    0.0250 United States of America
2   0.0136 1.88e-79  0.00876    0.0211 Venezuela               
3   0.0331 3.17e-38  0.0201     0.0541 Rwanda

Your Turn!


  • Run a multivariate logistic model using conflict onset as the outcome variable
  • Select alternative variables and/or alternative measures of the same variables
  • Interpret some of the coefficients
  • Calculate the precicted probability in a handful of country-years based on your analysis

Regression Tables

What’s in a Regression Table?

Regression Tables with modelsummary


  • Oftentimes we want to show multiple models at once (like F&L)
  • We want to compare across them and see which is the best model
  • How can we do that?
  • There are many ways to do this in R
  • We will use the modelsummary package

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

Table 1: Predictors of Conflict Onset
Ethnicity Democracy Terrain Full Model
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
See appendix for data sources.
Ethnic Frac 0.733* 0.709+
(0.365) (0.372)
Religions Frac -0.429 -0.288
(0.409) (0.409)
Polyarchy -0.801* -0.652
(0.390) (0.513)
Terrain 0.035 0.084
(0.075) (0.076)
Per capita GDP -0.485*** -0.275*** -0.553*** -0.392**
(0.104) (0.059) (0.092) (0.120)
Population 0.278*** 0.300*** 0.296*** 0.285***
(0.067) (0.051) (0.050) (0.068)
Intercept -4.571*** -6.184*** -4.166*** -5.376***
(1.323) (0.967) (1.140) (1.410)
Num.Obs. 6364 6955 6840 6129

Your Turn!


  • Got to the peacesciencer documentation
  • How close are our data to F&L’s?
  • Could we change something to better approximate their results?
  • Run multiple models using different predictors
  • Display the models using modelsummary
  • Try to get as close to F&L as you can!

Coefficient Plots


This don’t look too good…

Show the code 
modelsummary(conflict_model, 
             stars = TRUE,  
             gof_map = c("nobs"),
             coef_map = coef_map,
             title = caption, 
             notes = reference)
Table 1: Predictors of Conflict Onset
(1)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
See appendix for data sources.
Ethnic Frac 0.709+
(0.372)
Religions Frac -0.288
(0.409)
Polyarchy -0.652
(0.513)
Terrain 0.084
(0.076)
Per capita GDP -0.392**
(0.120)
Population 0.285***
(0.068)
Intercept -5.376***
(1.410)
Num.Obs. 6129


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

Show the code 
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!

  • Take one of your models
  • Use modelplot to create a coefficient plot of it
  • Customize the plot to your liking
  • Interpret the results
  • Discuss advantages of coefficient plots with a neighbor