examine function

Diagnostic functions

Provide diagnostic functions for an object of class result.goldfish. outliers helps to identify outliers events. changepoints helps to identify where a change point in the events sequence is presented using the log-likelihood.

examineOutliers(
  x,
  method = c("Hampel", "IQR", "Top"),
  parameter = 3,
  window = NULL
)

examineChangepoints(
  x,
  moment = c("mean", "variance"),
  method = c("PELT", "AMOC", "BinSeg"),
  window = NULL,
  ...
)

Arguments

• x: an object of class result.goldfish output from an estimate call.
• method: Choice of "AMOC", "PELT" or "BinSeg". For a detail description see cpt.mean or cpt.var. The default value is "PELT".
• parameter: An integer that represents the number of absolute outliers to identify, the threshold for the Hampel filter, i.e. parameter * MAD, or the threshold beyond the interquartile range halved, i.e. parameter/2 * IQR.
• window: The window half-width for the Hampel filter. By default it is half the width of the event sequence.
• moment: character argument to choose between "mean" or "variance". See section Change point for details.
• ...: additional arguments to be passed to the functions in the changepoint package.

Returns

NULL if neither outliers nor change points are identified. An object of class ggplot object from a call of ggplot2::ggplot(). It can be modified using the ggplot2 syntax.

Outliers

examineOutliers creates a plot with the log-likelihood of the events in the y-axis and the event index in the x-axis, identifying observations with labels indicating the sender and recipient.

Change point

The parameter moment controls which method from the package changepoint is used:

• "mean": It uses the cpt.mean

   function to investigate optimal positioning and (potentially) number of change points for the log-likelihood of the events in mean.
  

• "variance": It uses the cpt.var

   function to investigate optimal positioning and (potentially) number of change points for the log-likelihood of the events in variance
  

The function call creates a plot with the log-likelihood of the events in the y-axis and the event index in the x-axis, highlighting the change point sections identified by the method.

Examples

# A multinomial receiver choice model
data("Social_Evolution")
callNetwork <- defineNetwork(nodes = actors, directed = TRUE)
callNetwork <- linkEvents(
  x = callNetwork, changeEvent = calls,
  nodes = actors
)
callsDependent <- defineDependentEvents(
  events = calls, nodes = actors,
  defaultNetwork = callNetwork
)

mod01 <- estimate(callsDependent ~ inertia + recip + trans,
  model = "DyNAM", subModel = "choice",
  estimationInit = list(
    returnIntervalLogL = TRUE,
    engine = "default_c"
  )
)

examineOutliers(mod01)

examineChangepoints(mod01)