R: Wordscores text model

textmodel_wordscores {quanteda.textmodels} | R Documentation

## Wordscores text model

### Description

`textmodel_wordscores` implements Laver, Benoit and Garry's (2003) "Wordscores" method for scaling texts on a single dimension, given a set of anchoring or reference texts whose values are set through reference scores. This scale can be fitted in the linear space (as per LBG 2003) or in the logit space (as per Beauchamp 2012). Estimates of virgin or unknown texts are obtained using the `predict()` method to score documents from a fitted `textmodel_wordscores` object.

### Usage

    textmodel_wordscores(x, y, scale = c("linear", "logit"), smooth = 0)

### Arguments

`x` | the dfm on which the model will be trained
`y` | vector of training scores associated with each document in `x`
`scale` | scale on which to score the words; `"linear"` for classic LBG linear posterior weighted word class differences, or `"logit"` for log posterior differences
`smooth` | a smoothing parameter for word counts; defaults to zero to match the LBG (2003) method. See Value below for additional information on the behaviour of this argument.

### Details

The `textmodel_wordscores()` function and the associated `predict()` method are designed to function in the same manner as `stats::predict.lm()`. `coef()` can also be used to extract the word coefficients from the fitted `textmodel_wordscores` object, and `summary()` will print a nice summary of the fitted object.

### Value

A fitted `textmodel_wordscores` object. This object will contain a copy of the input data, but in its original form without any smoothing applied. Calling `predict.textmodel_wordscores()` on this object without specifying a value for `newdata`, for instance, will predict on the unsmoothed object. This behaviour differs from versions of quanteda <= 1.2.

### Author(s)

Kenneth Benoit

### References

Laver, M., Benoit, K.R., & Garry, J. (2003). Estimating Policy Positions from Political Text using Words as Data. American Political Science Review, 97(2), 311–331.

Beauchamp, N. (2012). Using Text to Scale Legislatures with Uninformative Voting. New York University Mimeo.

Martin, L.W. & Vanberg, G. (2007). A Robust Transformation Procedure for Interpreting Political Text. Political Analysis 16(1), 93–100. doi: 10.1093/pan/mpm010

### See Also

`predict.textmodel_wordscores()` for methods of applying a fitted textmodel_wordscores model object to predict quantities from (other) documents.

### Examples

    (tmod <- textmodel_wordscores(quanteda::data_dfm_lbgexample, y = c(seq(-1.5, 1.5, .75), NA)))
    summary(tmod)
    coef(tmod)
    predict(tmod)
    predict(tmod, rescaling = "lbg")
    predict(tmod, se.fit = TRUE, interval = "confidence", rescaling = "mv")

[Package quanteda.textmodels version 0.9.4 Index]