quanteda (version 0.9.0-1)

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 = "poisson")
## S3 method for class 'textmodel_wordfish_fitted':
print(x, n = 30L, ...)
## S3 method for class 'textmodel_wordfish_fitted':
show(object)
## S3 method for class 'textmodel_wordfish_predicted':
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

priors for $\theta_i$, $\alpha_i$, $\psi_j$, and $\beta_j$ where $i$ indexes documents and $j$ indexes features

tol

tolerances for convergence (explain why a pair)

dispersion

sets whether a quasi-poisson quasi-likelihood should be used based on a single dispersion parameter ("single"), dispersion parameters for each work ("byterm" or "bytermfloor"), or not ("none")

x

for print method, the object to be printed

n

max rows of dfm to print

...

additional arguments passed to other functions

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.

Examples

ie2010dfm <- dfm(ie2010Corpus, verbose=FALSE)
wfmodel <- textmodel_wordfish(LBGexample, dir = c(6,5))
wfmodel

if (require(austin)) {
        wfmodelAustin <- wordfish(quanteda::as.wfm(LBGexample), dir = c(6,5))
        cor(wfmodel@theta, wfmodelAustin$theta)
}