Description Usage Arguments Value Class hierarchy See Also Examples
Fit a text model to a dfm. Creates an object of virtual class
textmodel_fittedclass, whose exact properties (slots and methods)
will depend on which model was called (see model
types below).
1 2 3 4 5 6 7 8 9 10  textmodel(x, y = NULL, data = NULL, model = c("wordscores", "nb",
"wordfish", "ca"), ...)
## S4 method for signature 'dfm,ANY,missing,character'
textmodel(x, y = NULL, data = NULL,
model = c("wordscores", "nb", "wordfish", "ca"), ...)
## S4 method for signature 'formula,missing,dfm,character'
textmodel(x, y = NULL,
data = NULL, model = c("wordscores", "nb", "wordfish", "ca"), ...)

x 
a quanteda dfm object containing feature counts by document 
y 
for supervised models, a vector of class labels or values for
training the model, with 
data 
dfm or data.frame from which to take the formula 
model 
the model type to be fit. Currently implemented methods are:

... 
additional arguments to be passed to specific model types 
formula 
An object of class formula of the form 
a textmodel
class list, containing the fitted model and
additional information specific to the model class. See the methods for
specific models, e.g. textmodel_wordscores,
etc.
Here will go the description of the class hierarchy that governs dispatch for the predict, print, summary methods, since this is not terribly obvious. (Blame it on the S3 system.)
textmodel
, textmodel_wordscores
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ieDfm < dfm(data_corpus_irishbudget2010, verbose=FALSE)
refscores < c(rep(NA, 4), 1, 1, rep(NA, 8))
ws < textmodel(ieDfm, refscores, model="wordscores", smooth=1)
# alternative formula notation  but slower
# need the  1 to remove the intercept, as this is literal formula notation
wsform < textmodel(refscores ~ .  1, data=ieDfm, model="wordscores", smooth=1)
identical(ws@Sw, wsform@Sw) # compare wordscores from the two models
# compare the logit and linear wordscores
bs < textmodel(ieDfm[5:6,], refscores[5:6], model="wordscores", scale="logit", smooth=1)
plot(ws@Sw, bs@Sw, xlim=c(1, 1), xlab="Linear word score", ylab="Logit word score")
## Not run: wf < textmodel(ieDfm, model="wordfish", dir = c(6,5))
wf
## End(Not run)

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