textmodel_affinity: Class affinity maximum likelihood text scaling model

Description Usage Arguments Author(s) References See Also Examples

Description

textmodel_affinity implements the maximum likelihood supervised text scaling method described in Perry and Benoit (2017).

Usage

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textmodel_affinity(x, y, exclude = NULL, smooth = 0.5,
  ref_smooth = 0.5, verbose = quanteda_options("verbose"))

Arguments

x

the dfm or bootstrap_dfm object on which the model will be fit. Does not need to contain only the training documents, since the index of these will be matched automatically.

y

vector of training classes/scores associated with each document identified in data

exclude

a set of words to exclude from the model

smooth

a smoothing parameter for class affinities; defaults to 0.5 (Jeffreys prior). A plausible alternative would be 1.0 (Laplace prior).

ref_smooth

a smoothing parameter for token distributions; defaults to 0.5

verbose

logical; if TRUE print diagnostic information during fitting.

Author(s)

Patrick Perry and Kenneth Benoit

References

Perry, P.O. & Benoit, K.R. (2017). Scaling Text with the Class Affinity Model. arXiv:1710.08963 [stat.ML].

See Also

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

Examples

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(af <- textmodel_affinity(data_dfm_lbgexample, y = c("L", NA, NA, NA, "R", NA)))
predict(af)
predict(af, newdata = data_dfm_lbgexample[6, ])

## Not run: 
# compute bootstrapped SEs
dfmat <- bootstrap_dfm(data_corpus_dailnoconf1991, n = 10, remove_punct = TRUE)
textmodel_affinity(dfmat, y = c("Govt", "Opp", "Opp", rep(NA, 55)))

## End(Not run)

quanteda/quanteda documentation built on June 15, 2019, 8:36 a.m.