influence.predict.textmodel_affinity {quanteda.textmodels} | R Documentation |
Compute feature influence from a predicted textmodel_affinity object
Description
Computes the influence of features on scaled textmodel_affinity()
applications.
Usage
## S3 method for class 'predict.textmodel_affinity'
influence(model, subset = !train, ...)
Arguments
model |
a predicted textmodel_affinity() object |
subset |
whether to use all data or a subset (for instance, exclude the training set) |
... |
unused |
Value
a named list classed as influence.predict.textmodel_affinity that contains
-
norm
a document by feature class sparse matrix of normalised influence measures -
count
a vector of counts of each non-zero feature in the input matrix -
rate
the normalised feature count for each non-zero feature in the input matrix -
mode
an integer vector of 1 or 2 indicating the class which the feature is influencing, for each non-zero feature -
levels
a character vector of the affinity class labels -
subset
a logical vector indicating whether the document was included in the computation of influence;FALSE
for documents assigned a class label in training the model -
support
logical vector for each feature matching the same return from predict.textmodel_affinity
See Also
Examples
tmod <- textmodel_affinity(quanteda::data_dfm_lbgexample, y = c("L", NA, NA, NA, "R", NA))
pred <- predict(tmod)
influence(pred)