Nothing
#' \[experimental\] Compute polarity scores with different hyper-parameters
#'
#' A function to compute polarity scores of words by resampling hyper-parameters
#' from a fitted LSS model.
#' @param x a fitted textmodel_lss object.
#' @param what choose the hyper-parameter to resample in bootstrapping.
#' @param mode choose the type of the result of bootstrapping. If `coef`,
#' returns polarity scores; if `terms`, returns words sorted by
#' the polarity scores in descending order.
#' @param from,to,by passed to `seq()` to generate values for `k`; only used
#' when `what = "k"`.
#' @param ... additional arguments passed to `as.textmodel_lss()`.
#' @param verbose show messages if `TRUE`.
#' @export
#' @importFrom quanteda check_integer
bootstrap_lss <- function(x, what = c("seeds", "k"), mode = c("terms", "coef"),
from = 50, to = NULL, by = 50, verbose = FALSE, ...) {
what <- match.arg(what)
mode <- match.arg(mode)
from <- check_integer(from, min = 1, max = x$k)
if (!is.null(to)) {
to <- check_integer(to, min = 1, max = x$k)
} else {
to <- x$k
}
by <- check_integer(by, min = 1, max = x$k - 50)
if (verbose)
cat(sprintf("Fitting textmodel_lss with a different hyper-parameter...\n"))
if (what == "seeds") {
param <- names(x$seeds_weighted)
beta <- lapply(param, function(y) {
if (verbose) cat(sprintf(' seeds = "%s"\n', y))
as.textmodel_lss(x, seeds = y, terms = x$terms, ...)$beta
})
names(beta) <- param
} else {
param <- seq(from, to, by = by)
beta <- lapply(param, function(y) {
if (verbose) cat(sprintf(' k = %d\n', y))
as.textmodel_lss(x, seeds = x$seeds, terms = x$terms, slice = y, ...)$beta
})
names(beta) <- as.character(param)
}
if (mode == "terms") {
result <- sapply(beta, function(y) names(sort(y, decreasing = TRUE)))
} else {
result <- do.call(cbind, beta)
}
attr(result, "what") <- what
attr(result, "values") <- param
return(result)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.