An R-based guide for linguistic typologists interested in applying NOMINATE multidimensional scaling (MDS) techniques to linguistic data as presented in Croft and Poole, “Inferring universals from grammatical variation: multidimensional scaling for typological analysis” (Theoretical Linguistics 34.1-37, 2008).” [Abstract]
This guide provides a brief summary of an R-based workflow for model
implementation and the visualization of model results within the
ggplot data visualization framework. A cross-linguistic data set of
indefinite pronouns from Haspelmath (1997) is utilized (and made
available) here for demonstration purposes. For more thoughtful
discussions regarding theory, scaling procedures & model interpretation,
see reference section.
if (!require("pacman")) install.packages("pacman")
pacman::p_load(# anominate, -- no longer maintained --
wnominate,
pscl,
ggplot2,
knitr,
devtools,
ggrepel,
data.table)devtools::install_github("jaytimm/wnomadds")
library(wnomadds)Data set: A 9 x 140 data frame: Nine indefinite pronominal meanings, using data from 140 pronouns in 40 languages. Data are made available here.
## File paths will look differently for Windows/Mac
local_data <- '/home/jtimm/Desktop/data/'Load data set:
setwd(local_data)
raw_data <- read.csv("Indefprn13.txt",
sep="\t",
stringsAsFactors = FALSE)A portion of the data frame is presented below. Rows contain functions/meanings, and are analagous to legislators in the NOMINATE model. Columns contain language-specific grammatical forms, and are analagous to roll calls (ie, votes) in the NOMINATE model.
A value of 1 in the table below means that a given form expresses a particular meaning; a value of 6 means that a given form does not express that particular meaning. Missing data are specified with the value 9.
knitr::kable(raw_data[,1:9]) | X | X01n | X01e | X01i | X01j | X01jd | X02d | X02e | X02i |
|---|---|---|---|---|---|---|---|---|
| spec.know | 6 | 1 | 6 | 6 | 6 | 6 | 6 | 1 |
| spec.unkn | 6 | 1 | 1 | 6 | 6 | 6 | 6 | 1 |
| irr.nonsp | 6 | 1 | 1 | 6 | 6 | 1 | 6 | 1 |
| question | 6 | 1 | 1 | 1 | 6 | 1 | 1 | 1 |
| condit | 6 | 1 | 1 | 1 | 6 | 1 | 1 | 1 |
| indir.neg | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| compar | 6 | 6 | 1 | 1 | 1 | 1 | 1 | 6 |
| dir.neg | 1 | 6 | 6 | 6 | 6 | 6 | 6 | 6 |
| free.ch | 6 | 6 | 1 | 6 | 1 | 1 | 6 | 6 |
The first step is to transform the original data structure into a
rollcall object using the pscl package.
roll_obj <- pscl::rollcall(raw_data [,-1],
yea=1,
nay=6,
missing=9,
notInLegis=8,
vote.names = colnames(raw_data)[2:ncol(raw_data)],
legis.names = raw_data[,1])Then we fit three models using the wnominate function – one-, two- &
three-dimensional solutions.
ideal_points_1D <- wnominate::wnominate (roll_obj, dims = 1, polarity=c(1))
ideal_points_2D <- wnominate::wnominate (roll_obj, dims = 2, polarity=c(1,2))
ideal_points_3D <- wnominate::wnominate (roll_obj, dims = 3, polarity=c(1,2,3)) The resulting data structures are each comprised of seven elements:
names(ideal_points_1D)## [1] "legislators" "rollcalls" "dimensions" "eigenvalues" "beta"
## [6] "weights" "fits"
Correct classification and fitness statistics for each model are
extracted from the fits element, and summarized below:
list('1D' = ideal_points_1D$fits,
'2D' = ideal_points_2D$fits,
'3D' = ideal_points_3D$fits)## $`1D`
## correctclass1D apre1D gmp1D
## 86.559998 0.539726 0.756875
##
## $`2D`
## correctclass1D correctclass2D apre1D apre2D gmp1D
## 86.3199997 93.1999969 0.5315068 0.7671233 0.7596588
## gmp2D
## 0.8530398
##
## $`3D`
## correctclass1D correctclass2D correctclass3D apre1D apre2D
## 85.5999985 94.8800049 95.4400024 0.5068493 0.8246576
## apre3D gmp1D gmp2D gmp3D
## 0.8438356 0.7662863 0.8526035 0.9352897
Extract legislator coordinates (ie, ideal points) from one-dimensional model results.
d1 <- cbind(label=rownames(ideal_points_1D$legislators),
ideal_points_1D$legislators)
d1 <- d1[order(d1$coord1D),]Plot legislators (ie, grammatical functions) in one-dimensional space by rank.
ggplot() +
geom_text(data = d1,
aes(x=reorder(label, coord1D),
y=coord1D,
label=label),
size=4,
color = 'blue') +
# theme_classic() +
theme_minimal() +
labs(title="1D W-NOMINATE Plot") +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank())+
xlab('') + ylab('First Dimension')+
ylim(-1.1, 1.1)+
coord_flip()
We first build a simple “base” plot using legislator coordinates from
two-dimensional model results. Per wnominate convention, we add a unit
circle to specify model constraints. All subsequent plots are built on
this simple base plot.
base_2D <- ggplot(data = ideal_points_2D$legislators,
aes(x=coord1D,
y=coord2D)) +
geom_point(size= 1.5,
color = 'blue') +
annotate("path",
x=cos(seq(0,2*pi,length.out=300)),
y=sin(seq(0,2*pi,length.out=300)),
color='gray',
size = .25) +
xlab('First Dimension') +
ylab('Second Dimension')
base_2D + ggtitle('Two-dimensional base plot')
Add labels, a title, and change the theme.
base_2D +
ggrepel::geom_text_repel(
data = ideal_points_2D$legislators,
aes(label = rownames(ideal_points_2D$legislators)),
direction = "y",
hjust = 0,
size = 4,
color = 'blue') +
theme_classic() +
# theme_minimal() +
ggtitle("W-NOMINATE Coordinates") 
I have developed a simple R package, wnomadds, that facilitates the
plotting of roll call cutting lines and roll call polarities using
ggplot. While wnominate provides functionality for plotting cutting
lines, only plotting in base R is supported. The wnm_get_cutlines
function extracts cutting line coordinates from wnominate model
results, along with coordinates specifying the direction of majority Yea
votes for a given roll call (ie, vote polarity). Addtional details about
the package are available here.
with_cuts <- wnomadds::wnm_get_cutlines(ideal_points_2D,
rollcall_obj = roll_obj,
arrow_length = 0.05)A sample of the resulting data frame:
head(with_cuts)## Bill_Code x_1 y_1 x_2 y_2 x_1a
## 1: X01e 0.2133632 0.97697296 -0.7595687 -0.65042705 0.2947332
## 2: X01j 0.9537808 -0.30050308 -0.9999306 0.01178267 0.9693951
## 3: X01jd -0.3446922 0.93871577 -0.7581260 -0.65210812 -0.4242334
## 4: X01n -0.7141289 -0.70001426 -0.9934111 0.11460499 -0.6733979
## 5: X02d 0.9963522 -0.08533627 -0.4672491 0.88412571 0.9478791
## 6: X02e 0.9537808 -0.30050308 -0.9999306 0.01178267 0.9693951
## y_1a x_2a y_2a
## 1: 0.9283264 -0.6781987 -0.6990736
## 2: -0.2028175 -0.9843163 0.1094682
## 3: 0.9593875 -0.8376672 -0.6314364
## 4: -0.6860501 -0.9526802 0.1285691
## 5: -0.1585163 -0.5157222 0.8109456
## 6: -0.2028175 -0.9843163 0.1094682
base_2D +
ggrepel::geom_text_repel(
data = ideal_points_2D$legislators,
aes(label = rownames(ideal_points_2D$legislators)),
direction = "y",
hjust = 0,
size = 4,
color = 'blue') +
geom_segment(data = with_cuts,
aes(x = x_1, y = y_1,
xend = x_2, yend = y_2),
size = .25) + #cutting start to end
theme_minimal() +
labs(title="Cutting lines & W-NOMINATE Coordinates")
base_2D +
geom_segment(data=with_cuts,
aes(x = x_1, y = y_1,
xend = x_2, yend = y_2),
size = .25) + #cutting start to end
##ARROWS --
geom_segment(data=with_cuts,
aes(x = x_2, y = y_2,
xend = x_2a, yend = y_2a),
#cutting end to opposite arrow
color = 'red',
arrow = arrow(length = unit(0.2,"cm"))) +
geom_segment(data=with_cuts,
aes(x = x_1, y = y_1,
xend = x_1a, yend = y_1a),
#cutting start to opposite arrow
color = 'red',
arrow = arrow(length = unit(0.2,"cm")))+
##END ARROWS.
geom_text(data=with_cuts,
aes(x = x_1a, y = y_1a,
label = Bill_Code),
size=2.5,
nudge_y = 0.03,
check_overlap = TRUE) +
theme_minimal() +
labs(title = "W-NOMINATE Coordinates, cutting lines & roll call polarity")
selected <- c('X01e', 'X01j', 'X01jd', 'X01n')
subset_cuts <- subset(with_cuts, Bill_Code %in% selected)
base_2D +
ggrepel::geom_text_repel(
data = ideal_points_2D$legislators,
aes(label = rownames(ideal_points_2D$legislators)),
direction = "y",
hjust = 0,
size = 4,
color = 'blue') +
geom_segment(data=subset_cuts,
aes(x = x_1, y = y_1,
xend = x_2, yend = y_2),
size = .25) + #cutting start to end
##ARROWS --
geom_segment(data=subset_cuts,
aes(x = x_2, y = y_2,
xend = x_2a, yend = y_2a),
#cutting end to opposite arrow
color = 'red',
arrow = arrow(length = unit(0.2,"cm"))) +
geom_segment(data=subset_cuts,
aes(x = x_1, y = y_1,
xend = x_1a, yend = y_1a),
#cutting start to opposite arrow
color = 'red',
arrow = arrow(length = unit(0.2,"cm")))+
##END ARROWS.
geom_text(data=subset_cuts,
aes(x = x_1a, y = y_1a,
label = Bill_Code),
size=2.5,
nudge_y = 0.03,
check_overlap = TRUE) +
theme_minimal() +
labs(title = "W-NOMINATE Coordinates & selected cutting lines")
#Extract language code from language-specific grammatical forms
with_cuts$lang <- gsub('[A-Za-z]', '', with_cuts$Bill_Code)
#Filter cutting line data set to first six language codes.
facet_cuts <- subset(with_cuts, lang %in% c('01', '02', '03', '04', '05', '06'))
base_2D +
geom_segment(data=facet_cuts,
aes(x = x_1, y = y_1,
xend = x_2, yend = y_2),
size = .25) + #cutting start to end
##ARROWS --
geom_segment(data=facet_cuts,
aes(x = x_2, y = y_2,
xend = x_2a, yend = y_2a),
#cutting end to opposite arrow
color = 'red',
arrow = arrow(length = unit(0.2,"cm"))) +
geom_segment(data=facet_cuts,
aes(x = x_1, y = y_1,
xend = x_1a, yend = y_1a),
#cutting start to opposite arrow
color = 'red',
arrow = arrow(length = unit(0.2,"cm")))+
##END ARROWS.
theme_minimal() +
facet_wrap(~lang) +
coord_fixed()+
labs(title = "W-NOMINATE Coordinates & language-specific cutting lines")
Royce Carroll, Christopher Hare, Jeffrey B. Lewis, James Lo, Keith T. Poole and Howard Rosenthal (2017). Alpha-NOMINATE: Ideal Point Estimator. R package version 0.6. URL http://k7moa.c om/alphanominate.htm
Croft, W., & Poole, K. T. (2008). Inferring universals from grammatical variation: Multidimensional scaling for typological analysis. Theoretical linguistics, 34(1), 1-37.
Haspelmath, M. (1997). Indefinite pronouns. Oxford: Clarendon Press.
Poole, K. T. (2005). Spatial models of parliamentary voting. Cambridge University Press.
Keith Poole, Jeffrey Lewis, James Lo, Royce Carroll (2011). Scaling Roll Call Votes with wnominate in R. Journal of Statistical Software, 42(14), 1-21. URL http://www.jstatsoft.org/v42/i14/.
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