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Ken recommends you use textmodel_affinity() instead.
textmodel_affinity()
affinity(p, x, smooth = 0.5, verbose = FALSE)
a list containing:
coefficients point estimates of theta
coefficients
se (likelihood) standard error of theta
se
cov covariance matrix
cov
smooth values of the smoothing parameter
smooth
support logical indicating if the feature was included
support
word likelihoods within classes, estimated from training data
term-document matrix for document(s) to be scaled
a misnamed smoothing parameter, either a scalar or a vector equal in length to the number of documents
Patrick Perry
p <- matrix(c(c(5/6, 0, 1/6), c(0, 4/5, 1/5)), nrow = 3, dimnames = list(c("A", "B", "C"), NULL)) theta <- c(.2, .8) q <- drop(p %*% theta) x <- 2 * q (fit <- affinity(p, x))
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