quanteda (version 0.9.7-17)

textmodel_wordfish: wordfish text model

Description

Estimate Slapin and Proksch's (2008) "wordfish" Poisson scaling model of one-dimensional document positions using conditional maximum likelihood.

Usage

textmodel_wordfish(data, dir = c(1, 2), priors = c(Inf, Inf, 3, 1), tol = c(1e-06, 1e-08), dispersion = c("poisson", "quasipoisson"), dispersionLevel = c("feature", "overall"), dispersionFloor = 0)
"print"(x, n = 30L, ...)
"show"(object)
"show"(object)

Arguments

data

the dfm on which the model will be fit

dir

set global identification by specifying the indexes for a pair of documents such that $\hat{\theta}_{dir[1]} < \hat{\theta}_{dir[2]}$.

priors

prior precisions for the estimated parameters $\alpha_i$, $\psi_j$, $\beta_j$, and $\theta_i$, where $i$ indexes documents and $j$ indexes features

tol

tolerances for convergence. The first value is a convergence threshold for the log-posterior of the model, the second value is the tolerance in the difference in parameter values from the iterative conditional maximum likelihood (from conditionally estimating document-level, then feature-level parameters).

dispersion

sets whether a quasi-poisson quasi-likelihood should be used based on a single dispersion parameter ("poisson"), or quasi-Poisson ("quasipoisson")

dispersionLevel

sets the unit level for the dispersion parameter, options are "feature" for term-level variances, or "overall" for a single dispersion parameter

dispersionFloor

constraint for the minimal underdispersion multiplier in the quasi-Poisson model. Used to minimize the distorting effect of terms with rare term or document frequencies that appear to be severely underdispersed. Default is 0, but this only applies if dispersion = "quasipoisson".

x

for print method, the object to be printed

n

max rows of dfm to print

...

additional arguments passed to print

object

wordfish fitted or predicted object to be shown

Value

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

dir

global identification of the dimension

theta

estimated document positions

alpha

estimated document fixed effects

beta

estimated feature marginal effects

psi

estimated word fixed effects

docs

document labels

features

feature labels

sigma

regularization parameter for betas in Poisson form

ll

log likelihood at convergence

se.theta

standard errors for theta-hats

data

dfm to which the model was fit

Details

The returns match those of Will Lowe's R implementation of wordfish (see the austin package), except that here we have renamed words to be features. (This return list may change.) We have also followed the practice begun with Slapin and Proksch's early implementation of the model that used a regularization parameter of se$(\sigma) = 3$, through the third element in priors.

References

Jonathan Slapin and Sven-Oliver Proksch. 2008. "A Scaling Model for Estimating Time-Series Party Positions from Texts." American Journal of Political Science 52(3):705-772. Lowe, Will and Kenneth Benoit. 2013. "Validating Estimates of Latent Traits from Textual Data Using Human Judgment as a Benchmark." Political Analysis 21(3), 298-313. http://doi.org/10.1093/pan/mpt002

Examples

textmodel_wordfish(LBGexample, dir = c(1,5))

## Not run: 
# ie2010dfm <- dfm(ie2010Corpus, verbose = FALSE)
# (wfm1 <- textmodel_wordfish(ie2010dfm, dir = c(6,5)))
# (wfm2a <- textmodel_wordfish(ie2010dfm, dir = c(6,5), 
#                              dispersion = "quasipoisson", dispersionFloor = 0))
# (wfm2b <- textmodel_wordfish(ie2010dfm, dir = c(6,5), 
#                              dispersion = "quasipoisson", dispersionFloor = .5))
# plot(wfm2a@phi, wfm2b@phi, xlab = "Min underdispersion = 0", ylab = "Min underdispersion = .5",
#      xlim = c(0, 1.0), ylim = c(0, 1.0))
# plot(wfm2a@phi, wfm2b@phi, xlab = "Min underdispersion = 0", ylab = "Min underdispersion = .5",
#      xlim = c(0, 1.0), ylim = c(0, 1.0), type = "n")
# underdispersedTerms <- sample(which(wfm2a@phi < 1.0), 5)
# which(features(ie2010dfm) %in% names(topfeatures(ie2010dfm, 20)))
# text(wfm2a@phi, wfm2b@phi, wfm2a@features, 
#      cex = .8, xlim = c(0, 1.0), ylim = c(0, 1.0), col = "grey90")
# text(wfm2a@phi[underdispersedTerms], wfm2b@phi[underdispersedTerms], 
#      wfm2a@features[underdispersedTerms], 
#      cex = .8, xlim = c(0, 1.0), ylim = c(0, 1.0), col = "black")
# if (require(austin)) {
#     wfmodelAustin <- austin::wordfish(quanteda::as.wfm(ie2010dfm), dir = c(6,5))
#     cor(wfm1@theta, wfm1Austin$theta)
# }## End(Not run)