textmodel_wordshoal: Wordshoal text model

In kbenoit/wordshoal: quanteda textmodel extensions for the wordshoal textmodel

Description

Estimate Lauderdale and Herzog's (2016) model for one-dimensional document author (e.g. speakers) positions based on multiple groups of texts (e.g. debates). Each group of texts is scaled using Slapin and Proksch's (2008) "wordfish" Poisson scaling model of one-dimensional document positions, and then the positions from a particular author are scaled across groups using a second-level linear factor model, using conditional maximum likelihood.

Usage

textmodel_wordshoal(x, groups, authors, dir = c(1, 2), tol = 0.001)

Arguments

x	the quanteda dfm from which the model will be fit
groups	the name of a variable in the document variables for data giving the document group for each document
authors	the name of a variable in the document variables for data giving the author of each document
dir	set global identification by specifying the indexes for a pair of authors such that \hat{θ}_{dir[1]} < \hat{θ}_{dir[2]}
tol	a convergence threshold for the log-posterior of the model

Details

Returns estimates of relative author positions across the full corpus of texts.

Value

An object of class textmodel_wordshoal. This is a list containing:

tol	log-posterior tolerance used in fitting
dir	global identification of the dimension
theta	estimated document positions
beta	debate marginal effects
alpha	estimated document fixed effects
psi	estimated document debate-level positions
groups	document groups
authors	document authors
ll	log likelihood at convergence
se.theta	standard errors for theta-hats
data	corpus to which the model was fit

Author(s)

Benjamin Lauderdale and Kenneth Benoit

References

Benjamin E. Lauderdale and Alexander Herzog. 2016. "Measuring Political Positions from Legislative Speech." Political Analysis 24 (3, July): 374-394.

Examples

library("quanteda")
iedfm <- dfm(data_corpus_irish30, remove_punct = TRUE) 

wordshoalfit <- 
    textmodel_wordshoal(iedfm, dir = c(7,1),
                        groups = docvars(data_corpus_irish30, "debateID"), 
                        authors = docvars(data_corpus_irish30, "member.name"))
summary(wordshoalfit)                    
author_positions <- summary(wordshoalfit)$estimated.author.positions
author_positions$row_names <- rownames(author_positions)
fitdf <- merge(author_positions,
               docvars(data_corpus_irish30), 
               by.x = "row_names", by.y = "member.name")
fitdf <- subset(fitdf, !duplicated(memberID))
aggregate(theta ~ party.name, data = fitdf, mean)

kbenoit/wordshoal documentation built on May 8, 2020, 4:18 a.m.