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Introduction to dyadicMarkov

Source: vignettes/dyadicMarkov-introduction.Rmd
dyadicMarkov-introduction.Rmd

Purpose of the package

dyadicMarkov implements an R workflow for identifying patterns of interaction in categorical dyadic sequences using transition matrices. The package is designed for situations in which one or two categorical variables are observed repeatedly for both members of one dyad, so that the analysis accounts for both temporal dependence and dyadic dependence.

dyadicMarkov is based on three methodological papers on dyadic pattern analysis with the Longitudinal Actor-Partner Interdependence Model (L-APIM) and Markov chains. The univariate single-case method is described by Bollenrücher, Darwiche, and Antonietti (2023). The extension to visualization and clustering of similar dyadic behaviors is described by Bollenrücher, Darwiche, and Antonietti (2024). The bivariate single-case method is described by Böllenrücher, Darwiche, and Antonietti (in press). The visualization and clustering methodology of Bollenrücher, Darwiche, and Antonietti (2024) is methodological background; it is not currently part of the exported package API.

Real-world use cases

The package is intended for ordered categorical observations collected from two members of a dyad. In practice, such sequences may arise from coded interaction data, daily diary studies, repeated binary responses, or intensive longitudinal designs. For example, researchers may code whether each partner shows a given behavior at each measurement occasion, whether a parent and child are in one of several interaction states, or whether two individuals report the presence or absence of a response across repeated observations.

The package does not estimate a general-purpose time-series forecasting model. Instead, it summarizes how the next state of the member sequence analyzed is associated with its own previous state and with the previous state of the partner. The resulting pattern labels help describe whether the observed transitions are better characterized by actor dependence, partner dependence, actor-partner dependence, independence, or, in the bivariate workflow, by partial or complete bivariate dependence structures.

The example datasets included in the package are synthetic. They are used to make the required input structure reproducible and easy to inspect. They should be read as small stand-ins for real ordered dyadic sequences, not as substantive empirical datasets.

Data structure

The package works with categorical dyadic sequences. In the univariate case, one categorical variable is observed over time for two members of a dyad. For each function call, the first member is the member whose next state is modeled, and the second member supplies the partner sequence. The roles can be reversed to analyze the other member.

In the bivariate case, two categorical variables are observed over time for both members of the dyad. The current implementation of the bivariate workflow supports binary variables (states = 2). This leads to a bivariate empirical count matrix with 16 rows and 2 columns: the 16 rows represent the four binary lagged components: the first member on the main variable, the second member on the main variable, the first member on the second variable and the second member on the second variable. The 2 columns represent the possible next states of the first member on the main variable.

Estimation and identification

The package separates estimation from identification. Estimation summarizes the observed sequences as empirical transition counts and maximum-likelihood transition probabilities. Identification compares the observed transition structure with restricted transition structures corresponding to interpretable patterns of interaction.

In the univariate workflow, the relevant patterns are actor-partner, actor-only, partner-only and independence. In the bivariate workflow, the analysis first identifies the global case as trivial, univariate, partial bivariate or complete bivariate. A trivial case has no subsequent local pattern. A univariate case is followed by univariatePattern() on the main-variable sequences. Partial and complete cases are followed by partialPattern() and completePattern(), respectively.

Exported functions

The user-facing workflow is organized around seven exported functions:

  • countEmp() computes empirical transition counts for the first member sequence in a univariate dyadic sequence.
  • mleEstimation() estimates transition probabilities from empirical count matrices.
  • univariatePattern() identifies the univariate interaction pattern.
  • countEmpBivariate() computes empirical transition counts for the first member sequence in a bivariate dyadic sequence.
  • bivariateCase() identifies the global dependence case for the member sequence analyzed.
  • partialPattern() selects a local pattern for a partial bivariate case.
  • completePattern() selects a local pattern for a complete bivariate case.

Assumptions and current scope

The workflow assumes categorical states coded as integers from 1 to states, equal chain lengths, ordered repeated observations, and a first-order homogeneous transition process. Inputs containing NA are rejected; missing observations are not deleted or imputed automatically because they break the construction of transition pairs.

The bivariate functions currently support the binary-state case. With two binary variables observed for two members, the previous state is described by four binary components, producing 2^4 = 16 previous-state combinations. Generalizing this workflow beyond states = 2 would require extending the bivariate restriction structures and their implementation.

Relationship to the workflow vignettes

This introduction explains the scope and structure of the package. The univariate workflow vignette shows the use of countEmp(), mleEstimation() and univariatePattern(). The bivariate workflow vignette shows the use of countEmpBivariate(), bivariateCase(), partialPattern() and completePattern().

References

Bollenrücher, Mégane, Joëlle Darwiche, and Jean-Philippe Antonietti. 2023. “Dyadic Pattern Analysis Using Longitudinal Actor-Partner Interdependence Model with Markov Chains for Unique Case Analysis.” The Quantitative Methods for Psychology 19 (3): 230-43. https://doi.org/10.20982/tqmp.19.3.p230.

Bollenrücher, Mégane, Joëlle Darwiche, and Jean-Philippe Antonietti. 2024. “Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model with Markov Chains.” The Quantitative Methods for Psychology 20 (1): 17-32. https://doi.org/10.20982/tqmp.20.1.p017.

Böllenrücher, Mégane, Joëlle Darwiche, and Jean-Philippe Antonietti. in press. “Bivariate Dyadic Patterns Analysis Using Longitudinal Actor-Partner Interdependence Model and Markov Chains for Single-Case.” Quantitative and Computational Methods in Behavioral Sciences, in press. https://doi.org/10.23668/psycharchives.22174.