Estimate \(\theta_i\) for each document, from a fitted textmodel_affinity object.
Other methods below provide standard ways to extract or compute quantities from predicted textmodel_affinity objects.
# S3 method for textmodel_affinity
predict(object, newdata = NULL, level = 0.95, ...)# S3 method for predict.textmodel_affinity
coef(object, ...)
# S3 method for predict.textmodel_affinity
residuals(object, type = c("response", "pearson"), ...)
# S3 method for predict.textmodel_affinity
rstandard(model, ...)
predict() returns a list of predicted affinity textmodel
quantities, containing:
coefficients a numeric matrix of affinity estimates (coefficients) for
each class (columns) for each document (rows)
se a numeric matrix of likelihood standard errors for affinity coefficients
each class (columns) for each document (rows)
cov an array of covariance matrices for each affinity class, one per document
smooth a numeric vector of length two for the smoothing parameters smooth
and ref_smooth from textmodel_affinity()
newdata a dfm on which prediction has been made
train a logical vector indicating which documents were used in training the model
level the confidence level for computing standard errors
p the p return from textmodel_affinity
support logical vector indicating whether a feature was included in computing
class affinities
coef() returns a document \(\times\) class matrix of class
affinities for each document.
residuals() returns a document-by-feature matrix of residuals.
resid() is an alias.
rstandard() is a shortcut to return the Pearson residuals.
a fitted affinity textmodel
dfm on which prediction should be made
probability level for confidence interval width
unused
see residuals.lm
influence.predict.textmodel_affinity() for methods of
computing the influence of particular features from a predicted
textmodel_affinity model.