Introduction to roundRobinR
In roundRobinR: Manipulate and Analyze Round Robin Dyadic Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>"
)

library(roundRobinR)

Overview

The roundRobinR package provides tools for analyzing directed dyadic data collected with a round-robin design, in which every person in a group rates or interacts with every other person. The core analytical framework is the Social Relations Model (SRM; Kenny, Kashy, & Cook, 2006), estimated via multilevel modeling following Snijders and Kenny (1999) and Knight and Humphrey (2019).

The SRM decomposes the total variance in a directed dyadic outcome into four components:

| Component | Interpretation | |-----------|----------------| | Group | How much groups differ in their mean level | | Actor | How much people differ in what they give or emit | | Partner | How much people differ in what they receive or elicit | | Dyad | Variance in the unique relationship between each specific pair |

In addition, two reciprocity parameters are estimated:

Generalized reciprocity: Do people who give a lot also tend to receive a lot? (Actor-partner correlation)
Dyadic reciprocity: Does person A's unique relationship with person B predict B's unique relationship with A? (Within-dyad relationship correlation)

The Sample Dataset

The package includes sampleDyadData, a simulated dataset with 1,548 rows from a round-robin design collected at two time points.

head(sampleDyadData)
nrow(sampleDyadData)
length(unique(sampleDyadData$groupId))

Key variables:

groupId, actId, partId — group, actor, and partner identifiers
timeId — time point (1 or 2)
liking — directed dyadic rating of liking (the main outcome)
actEx, partEx — actor and partner extraversion scores
contact — dyad-level contact frequency
groupCohesion — group-level cohesion

Step 1: Create Dummy Variables

The SRM requires a set of dummy variables for each actor and partner position within a group. createDummies() generates these automatically.

d <- createDummies(
  group.id       = "groupId",
  act.id         = "actId",
  part.id        = "partId",
  d              = sampleDyadData[sampleDyadData$timeId == 1, ],
  merge.original = TRUE
)
head(d[, c("groupId", "actId", "partId", "pdSRM_dyad_id",
           "a1", "a2", "a3", "a4", "p1", "p2", "p3", "p4")])

The key output columns are:

a1–a4: actor dummy variables (one equals 1 per row, indicating which group position is acting)
p1–p4: partner dummy variables (same, for the partner)
pdSRM_dyad_id: undirected dyad identifier (shared by i→j and j→i)

Step 2: Fit the Null SRM

The simplest model decomposes total variance into the four SRM components with no predictors. srmRun() handles dummy creation and model fitting in one step.

null_mod <- srmRun(
  dv      = "liking",
  groupId = "groupId",
  actId   = "actId",
  partId  = "partId",
  data    = sampleDyadData[sampleDyadData$timeId == 1, ]
)
null_mod$srm.output

Reading the output:

variances.and.covariances: raw variance estimates for each component
percents.and.correlations: for the four variance components, the percentage of total variance; for the two reciprocity rows, the correlation coefficients

Step 3: Fit a Model with Predictors

Adding fixed effects tests whether actor-level, partner-level, or dyad-level predictors explain variance in the outcome.

full_mod <- srmRun(
  dv      = "liking",
  groupId = "groupId",
  actId   = "actId",
  partId  = "partId",
  feVars  = c("actEx", "partEx", "contact"),
  data    = sampleDyadData[sampleDyadData$timeId == 1, ]
)
full_mod$srm.output

The fixed-effect estimates can be examined with:

summary(full_mod$lme.output)$tTable

Step 4: Pseudo R-Squared

srmPseudoRSq() compares the null and full models to estimate the proportion of variance in each component explained by the predictors.

srmPseudoRSq(
  null.model    = null_mod$lme.output,
  predict.model = full_mod$lme.output
)

A positive pseudoR2 for a given component means the fixed effects reduced that component's variance, suggesting the predictors partly explain the group-, actor-, partner-, or relationship-level variation.

References

Kenny, D. A., Kashy, D. A., & Cook, W. L. (2006). Dyadic Data Analysis. Guilford Press.

Knight, A. P., & Humphrey, S. E. (2019). Dyadic data analysis. In S. E. Humphrey & J. M. LeBreton (Eds.), The Handbook for Multilevel Theory, Measurement, and Analysis (pp. 423–447). American Psychological Association. https://doi.org/10.1037/0000115-019

Snijders, T. A. B., & Kenny, D. A. (1999). The social relations model for family data: A multilevel approach. Personal Relationships, 6, 471–486. https://doi.org/10.1111/j.1475-6811.1999.tb00204.x