filterFoc: Filter by First Order Parameters
In montesmariana/semcloud: Post-processing of token-level clouds

filterFoc

R Documentation

Filter by First Order Parameters

Description

Filter by First Order Parameters

Usage

filterFoc(
  foc_param,
  tid_data,
  cw_selection,
  is_dep_fun = function(foc_param) stringr::str_starts(foc_param, "LEMMA"),
  max_steps_fun = function(foc_param) if (foc_param == "LEMMAPATH2") 2 else 3,
  window_filter_fun = windowFilter,
  pos_filter_fun = posFilter,
  bound_filter_fun = function(foc_param) stringr::str_starts(foc_param, "nobound")
)

Arguments

`foc_param`	Character string coding the relevant first-order parameters.
`tid_data`	Subsection of a context-word-by-token dataframe, as outputted by `setupConcordancer`, with information for one token.
`cw_selection`	Vector of context words selected by the model for that token.
`is_dep_fun`	Function that takes `foc_param` as input and returns `TRUE` if dependency information should be collected and `FALSE` if the model is based on bag-of-words instead.
`max_steps_fun`	Function that takes `foc_param` as input and returns, for dependency-based models, the maximum number of steps in the dependency path to accept as viable context words.
`window_filter_fun`	Function that takes `foc_param` as input and returns a vector or list with two elements: the left and right window sizes (for bag-of-words models).
`pos_filter_fun`	Function that takes `foc_param` as input and returns a vector. If the vector is empty, no `pos` filter is implemented, while if it has values, the rows with `pos` included in that vector will be selected.
`bound_filter_fun`	Function that takes `foc_param` as input and returns `TRUE` if words outside the sentence are modelled and `FALSE` if they are not.