remExpWeights: Helper Function to Compute Minimum Effective Time and...

In dream: Dynamic Relational Event Analysis and Modeling

Helper Function to Compute Minimum Effective Time and Exponential Weights for REM Statistics

Description

A helper function for computing exponential decay weights and the corresponding minimum effective time used to calculate network statistics in relational event models within the dream package. This implementation follows the formulations of Lerner et al. (2013) and Lerner & Lomi (2020). Although primarily designed for internal use (e.g., within computeReciprocity), it may also be of interest to users working directly with REM statistics (e.g., creating new statistics).

Usage

remExpWeights(
  current,
  past = NULL,
  halflife,
  dyadic_weight,
  Lerneretal_2013 = FALSE,
  exp.weights = TRUE
)

Arguments

current	The current relational event time.
past	The numeric vector of past event times (for exponential weighting only).
halflife	The halflife parameter for exponential weighting.
dyadic_weight	The dyadic (event) weight cutoff for relational relevancy.
Lerneretal_2013	TRUE/FALSE. If TRUE, the function uses the Lerner et al. (2013) exponential weighting function. If FALSE, the function uses the Lerner and Lomi (2020) exponential weighting function.
exp.weights	TRUE/FALSE. If TRUE, the function computes the exponential weights for past relational events. If FALSE, the function computes the minimum effective time for a relational event (that is, the minimum past time that would result in a 0 value for an exponential weight).

Details

• Exponential Weighting Function:

  • Lerner & Lomi (2020): w(u,a,t) = \sum \exp(- (t - t') * (\log(2)/T_{1/2}))

  • Lerner et al. (2013): w(u,a,t) = \sum \exp(- (t - t') * (\log(2)/T_{1/2})) * (\log(2)/T_{1/2})

• Minimum Effective Time (MEF):

  • Lerner & Lomi (2020): MEF = t + \log(w) / (\log(2)/T_{1/2})

  • Lerner et al. (2013): MEF = t + [T_{1/2} * \log((w * T_{1/2}) / \log(2))] / \log(2)

Value

When exp.weights = TRUE, the numeric vector of exponential decay weights. When exp.weights = FALSE, the scalar for the minimum event cut-off time.

Author(s)

Kevin A. Carson [e-mail], Diego F. Leal [e-mail]

References

Lerner, Jürgen and Alessandro Lomi. 2020. “Reliability of relational event model estimates under sampling: How to fit a relational event model to 360 million dyadic events.” Network Science 8(1): 97-135.

Lerner, Jürgen, Margit Bussman, Tom A.B. Snijders, and Ulrik Brandes. 2013. " Modeling Frequency and Type of Interaction in Event Networks." The Corvinus Journal of Sociology and Social Policy 4(1): 3-32.

dream documentation built on Aug. 8, 2025, 6:36 p.m.