The extended_uds dataset can be generated with the following code:

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.4.2

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(QuickUDS)

## Loading required package: mirt

## Warning: package 'mirt' was built under R version 3.4.3

## Loading required package: stats4

## Loading required package: lattice

measures <- c("pmm_arat", "blm", 
              "bmr_democracy_femalesuffrage",
              "bnr_extended", "pmm_bollen", "csvmdi",
              "doorenspleet", "eiu",
              "wgi_democracy","fh_total_reversed", 
              "fh_electoral", "gwf_democracy_extended", 
              "pmm_hadenius", "kailitz_tri",
              "lexical_index", "mainwaring", 
              "magaloni_democracy_extended",
              "pmm_munck", "pacl", "PEPS1v",
              "pitf", "polity2", "reign_democracy", 
              "polyarchy_original_contestation",
              "prc", "svolik_democracy", 
              "ulfelder_democracy_extended",
              "utip_dichotomous_strict", "v2x_polyarchy",
              "vanhanen_democratization", "wth_democ1")

# Generate Extended UDS

extended_model <- democracy_model(measures, 
                                  verbose = FALSE, 
                                  technical = list(NCYCLES = 2500))

## Warning: Log-likelihood was decreasing near the ML solution. EM method may
## be unstable

extended_scores <- democracy_scores(model = extended_model)

The model takes about 1-2 minutes to converge on my machine, and accounts for more than 90 per cent of the variance in the various democracy indexes used:

extended_model@time

## TOTAL:   Data  Estep  Mstep     SE   Post 
##  68.45   1.24   9.59  43.90  13.62   0.00

# Number of iterations, log likelihood, etc.
extended_model

## 
## Call:
## mirt::mirt(data = data %>% select(cols$measures), model = 1, 
##     itemtype = "graded", SE = TRUE, verbose = verbose, technical = technical)
## 
## Full-information item factor analysis with 1 factor(s).
## Converged within 1e-04 tolerance after 343 EM iterations.
## mirt version: 1.26.3 
## M-step optimizer: BFGS 
## EM acceleration: Ramsay 
## Number of rectangular quadrature: 61
## 
## Information matrix estimated with method: Oakes
## Condition number of information matrix = 15874.99
## Second-order test: model is a possible local maximum
## 
## Log-likelihood = -230681.4
## Estimated parameters: 229 
## AIC = 461820.9; AICc = 461824.8
## BIC = 463700.6; SABIC = 462972.8

# Correlations of latent factor with source variables, variance accounted for, etc.
summary(extended_model)

##                                    F1    h2
## pmm_arat                        0.904 0.818
## blm                             0.981 0.961
## bmr_democracy_femalesuffrage    0.976 0.953
## bnr_extended                    0.964 0.929
## pmm_bollen                      0.927 0.860
## csvmdi                          0.942 0.888
## doorenspleet                    0.972 0.944
## eiu                             0.955 0.912
## wgi_democracy                   0.959 0.919
## fh_total_reversed               0.937 0.878
## fh_electoral                    0.985 0.971
## gwf_democracy_extended          0.954 0.909
## pmm_hadenius                    0.956 0.915
## kailitz_tri                     0.939 0.882
## lexical_index                   0.923 0.853
## mainwaring                      0.971 0.943
## magaloni_democracy_extended     0.969 0.938
## pmm_munck                       0.939 0.881
## pacl                            0.956 0.914
## PEPS1v                          0.991 0.982
## pitf                            0.971 0.942
## polity2                         0.985 0.969
## reign_democracy                 0.950 0.903
## polyarchy_original_contestation 0.946 0.896
## prc                             0.956 0.914
## svolik_democracy                0.959 0.919
## ulfelder_democracy_extended     0.969 0.939
## utip_dichotomous_strict         0.951 0.905
## v2x_polyarchy                   0.945 0.893
## vanhanen_democratization        0.938 0.879
## wth_democ1                      0.991 0.983
## 
## SS loadings:  28.391 
## Proportion Var:  0.916 
## 
## Factor correlations: 
## 
##    F1
## F1  1

Cutpoints and Discrimination

The cutpoints and discrimination parameters of these scores can be obtained as follows:

library(ggplot2)

dichotomous <- cutpoints(extended_model) %>% 
  group_by(variable) %>% 
  filter(n()==1) %>% 
  ungroup() %>% 
  summarise(avg_estimate = mean(estimate),
            avg_se = sqrt(sum(se^2)/n() ^ 2 )) %>%
  mutate(avg_pct975 = avg_estimate + 1.96*avg_se,
         avg_pct025 = avg_estimate - 1.96*avg_se)

xmax <- cutpoints(extended_model) %>% 
  pull(variable) %>% 
  unique() %>% 
  length()

xmax <- xmax + 1

cutpoints(extended_model) %>%
  ggplot(aes(x=variable, 
             y = estimate, 
             ymin = pct025,
             ymax=pct975))  + 
  theme_bw() + 
  geom_rect(ymin = dichotomous$avg_pct025, ymax = dichotomous$avg_pct975,
            xmin = 0, xmax = xmax, alpha = 0.2, fill = "grey") + 
  geom_hline(yintercept = dichotomous$avg_estimate, 
             color="black", linetype = 2) + 
  labs(x = "",
       y = "Unified democracy level rater cutoffs",
       title = "Rater cutoffs",
       subtitle = "Grey band represents the average cutoff for dichotomous measures of democracy") + 
  geom_point() + 
  geom_errorbar() + 
  geom_hline(yintercept = 0, 
             color="red")+
  coord_flip()

cutpoints(extended_model, type ="discrimination") %>%
  ggplot(aes(x=reorder(variable, estimate),
           y = estimate, ymin = pct025, ymax = pct975))  + 
  theme_bw() + 
  labs(x="",y="Discrimination parameter for each rater
       \n(higher value means fewer idiosyncratic\nerrors relative to latent score)",
       title = "Discrimination parameters",
       subtitle = "Measures with higher discrimination are less likely \nto make idiosyncratic judgments relative to the rest") + 
  geom_point() + 
  geom_errorbar() + 
  coord_flip()

Score types and scores by country

The democracy_scores function produces three types of scores: the original latent variable (z1), an “adjusted” score where 0 equals the cutpoint of the dichotomous democracy indexes (z1_adj) and a probability score (z1_*_as_prob) that is normalizes the other two to a 0-1 index, and can be interepreted as a kind of probability measure: country-years with scores close to 1 are almost certainly democratic, while country-years with scores close to 0 are almost certainly not, while 0.5 represents the cut-off between democracy and non-democracy.

These extended UD scores are available for 27133 country-years (248 unique countries and non-sovereign territories):

(Grey shaded areas represent 95% confidence intervals; blue shaded areas are periods where the country is either deemed to be a member of the system of states in the Gleditsch and Ward list of state system membership since 1816, i.e., independent, or is a microstate in Gleditsch’s tentative list).