Wrapper to generate an ENA model — ena.set.creator • rENA

Generates an ENA model by constructing a dimensional reduction of adjacency (co-occurrence) vectors as defined by the supplied conversations, units, and codes.

Usage

ena.set.creator(
  data,
  codes,
  units,
  conversation,
  metadata = NULL,
  model = c("EndPoint", "AccumulatedTrajectory", "SeparateTrajectory"),
  weight.by = "binary",
  window = c("MovingStanzaWindow", "Conversation"),
  window.size.back = 1,
  include.meta = TRUE,
  groupVar = NULL,
  groups = NULL,
  runTest = FALSE,
  ...
)

Arguments

data: data.frame with containing metadata and coded columns
codes: vector, numeric or character, of columns with codes
units: vector, numeric or character, of columns representing units
conversation: vector, numeric or character, of columns to segment conversations by
metadata: vector, numeric or character, of columns with additional meta information for units
model: character: EndPoint (default), AccumulatedTrajectory, SeparateTrajectory
weight.by: "binary" is default, can supply a function to call (e.g. sum)
window: MovingStanzaWindow (default) or Conversation
window.size.back: Number of lines in the stanza window (default: 1)
include.meta: [TBD]
groupVar: vector, character, of column name containing group identifiers. If column contains at least two unique values, will generate model using a means rotation (a dimensional reduction maximizing the variance between the means of the two groups)
groups: vector, character, of values of groupVar column used for means rotation or statistical tests
runTest: logical, TRUE will run a Student's t-Test and a Wilcoxon test for groups defined by the groups argument
...: Additional parameters passed to model generation

Value

ena.set object

Details

This function generates an ena.set object given a data.frame, units, conversations, and codes. After accumulating the adjacency (co-occurrence) vectors, computes a dimensional reduction (projection), and calculates node positions in the projected ENA space. Returns location of the units in the projected space, as well as locations for node positions, and normalized adjacency (co-occurrence) vectors to construct network graphs. Includes options for returning statistical tests between groups of units.