# textmodel_wordfish: Wordfish text model In quanteda.textmodels: Scaling Models and Classifiers for Textual Data

## Description

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

## Usage

  1 2 3 4 5 6 7 8 9 10 11 12 13 textmodel_wordfish( x, dir = c(1, 2), priors = c(Inf, Inf, 3, 1), tol = c(1e-06, 1e-08), dispersion = c("poisson", "quasipoisson"), dispersion_level = c("feature", "overall"), dispersion_floor = 0, sparse = FALSE, abs_err = FALSE, svd_sparse = TRUE, residual_floor = 0.5 ) 

## Arguments

 x 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{θ}_{dir[1]} < \hat{θ}_{dir[2]}. priors prior precisions for the estimated parameters α_i, ψ_j, β_j, and θ_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") dispersion_level sets the unit level for the dispersion parameter, options are "feature" for term-level variances, or "overall" for a single dispersion parameter dispersion_floor 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". sparse specifies whether the "dfm" is coerced to dense. While setting this to TRUE will make it possible to handle larger dfm objects (and make execution faster), it will generate slightly different results each time, because the sparse SVD routine has a stochastic element. abs_err specifies how the convergence is considered svd_sparse uses svd to initialize the starting values of theta, only applies when sparse = TRUE residual_floor specifies the threshold for residual matrix when calculating the svds, only applies when sparse = TRUE

## 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(σ) = 3, through the third element in priors.

## 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 x dfm to which the model was fit

## Note

In the rare situation where a warning message of "The algorithm did not converge." shows up, removing some documents may work.

## Author(s)

Benjamin Lauderdale, Haiyan Wang, and Kenneth Benoit

## References

Slapin, J. & Proksch, S.O. (2008). A Scaling Model for Estimating Time-Series Party Positions from Texts. doi: 10.1111/j.1540-5907.2008.00338.x. American Journal of Political Science, 52(3), 705–772.

Lowe, W. & Benoit, K.R. (2013). Validating Estimates of Latent Traits from Textual Data Using Human Judgment as a Benchmark. doi: 10.1093/pan/mpt002. Political Analysis, 21(3), 298–313.

predict.textmodel_wordfish()

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 (tmod1 <- textmodel_wordfish(quanteda::data_dfm_lbgexample, dir = c(1,5))) summary(tmod1, n = 10) coef(tmod1) predict(tmod1) predict(tmod1, se.fit = TRUE) predict(tmod1, interval = "confidence") ## Not run: library("quanteda") dfmat <- dfm(tokens(data_corpus_irishbudget2010)) (tmod2 <- textmodel_wordfish(dfmat, dir = c(6,5))) (tmod3 <- textmodel_wordfish(dfmat, dir = c(6,5), dispersion = "quasipoisson", dispersion_floor = 0)) (tmod4 <- textmodel_wordfish(dfmat, dir = c(6,5), dispersion = "quasipoisson", dispersion_floor = .5)) plot(tmod3$phi, tmod4$phi, xlab = "Min underdispersion = 0", ylab = "Min underdispersion = .5", xlim = c(0, 1.0), ylim = c(0, 1.0)) plot(tmod3$phi, tmod4$phi, xlab = "Min underdispersion = 0", ylab = "Min underdispersion = .5", xlim = c(0, 1.0), ylim = c(0, 1.0), type = "n") underdispersedTerms <- sample(which(tmod3$phi < 1.0), 5) which(featnames(dfmat) %in% names(topfeatures(dfmat, 20))) text(tmod3$phi, tmod4$phi, tmod3$features, cex = .8, xlim = c(0, 1.0), ylim = c(0, 1.0), col = "grey90") text(tmod3$phi['underdispersedTerms'], tmod4$phi['underdispersedTerms'], tmod3$features['underdispersedTerms'], cex = .8, xlim = c(0, 1.0), ylim = c(0, 1.0), col = "black") if (requireNamespace("austin")) { tmod5 <- austin::wordfish(quanteda::as.wfm(dfmat), dir = c(6, 5)) cor(tmod1$theta, tmod5\$theta) } ## End(Not run) 

quanteda.textmodels documentation built on April 6, 2021, 9:06 a.m.