getSampledVocalCountMatrix: getSampledVocalCountMatrix: generate vocalisation diagrams

Description

Generate a count vocalisation diagram through 'sampling'.

Usage

getSampledVocalCountMatrix(
  cdf,
  rate = 1,
  individual = FALSE,
  noPauseTypes = FALSE,
  begin = "begin",
  end = "end",
  nodecolumn = "role"
)

Value

a vocaldia object, consisting of a vocalisation matrix (vocmatrix) where cell <m,n> contains the counts of transitions from node n to node m, and a table of prior probabilities (stationary distribution) per node.

Arguments

cdf: a data frame consisting, minimally, of a column for vocalisation/pause start times, a column for end times, and a column identifying the speaker, speaker role or 'Floor' (for silences).
rate: the rate at which to sample the vocalisation events (in seconds)
individual: whether to include individual speakers or group them into a single Vocalisation node
noPauseTypes: if TRUE, ignore distinctions between pauses (SwitchingPause, GrpSwitchingPause, etc)
begin: the name of the column containing the start time of the vocalisation event in a row.
end: the name of the column containing the end time of the vocalisation event in the same row.
nodecolumn: the name of the column containing the node (speaker) name (e.g. 'speaker', 'role').

Details

A vocalisation diagram (vocaldia) is a representation of a dialogue as a Markov process whose cell <m,n> contains the transition probability from node n to node m). This function for 'cases' (an identifier for a case or a vector of identifiers identifying a set of cases) in data frame 'df', obtained by sampling the timeline every 'rate'-th second (see getSampledVocalCountMatrix).

Examples

Run this code

data(vocdia) 
getSampledVocalCountMatrix(subset(atddia,
     id=='Abbott_Maddock_01'), nodecolumn='role')