validate_dyad_data: Validate dyadic input data

In dyadMLM: Tools for Dyadic Multilevel Models

Validate dyadic input data

Description

Checks whether data has a valid long-format dyadic structure and returns it as a tibble with an additional dyadMLM_data class. Cross-sectional data may contain at most one row per member within each dyad. Intensive longitudinal data may contain at most one row per member and measurement occasion within each dyad.

Usage

validate_dyad_data(
  data,
  dyad,
  member,
  role = NULL,
  time = NULL,
  predictors = NULL,
  lag1_predictors = NULL,
  model_types = "apim",
  dsm_role_order = NULL,
  temporal_decomposition = c("auto", "2l", "none"),
  incomplete_dyads = c("error", "drop"),
  missing_role = c("error", "drop")
)

Arguments

data	A long-format data frame or tibble.
dyad	Column identifying the dyad.
member	Column identifying the two people or members within each dyad, such as a person ID.
role	Optional column identifying a stable member role, such as gender. Non-missing values must be consistent within each dyad x member. In repeated-measures data, an observed role is propagated to missing rows for the same member within a dyad. If no role is supplied, all dyads are treated as exchangeable.
time	Optional column identifying time or measurement order.
predictors	Optional variables to select and store as metadata for temporal predictor decomposition and model-helper functions.
lag1_predictors	Optional subset of predictors for which lag-1 model-ready columns should be created. Requires a finite, integer-valued numeric time variable.
model_types	Requested model-ready column families. Can contain one or more of "apim", "dim", and "dsm". "none" indicates no model-specific predictor construction and must be used alone.
dsm_role_order	For model_types = "dsm", a character vector giving the two distinguishable roles in the order used for directional differences. Required when DSM columns are requested and must be NULL otherwise.
temporal_decomposition	Requested temporal predictor decomposition strategy for predictors. "none" leaves predictors undecomposed before model-specific columns are constructed. "2l" indicates a two-level temporal predictor decomposition into within-person and between-person components. "auto" resolves to "2l" when both time and predictors are supplied, and to "none" otherwise. Model-specific helpers may apply additional conventions, such as grand-mean centering raw DIM and DSM dyad means.
incomplete_dyads	How to handle dyads with fewer than two unique members across all rows in data. "error" stops with an error and "drop" removes the entire dyad. A dyad with more than two unique members is invalid and always causes an error, regardless of this setting.
missing_role	How to handle dyads in which at least one member has no non-missing role value on any row. A consistent non-missing role observed for a member is propagated to that member's other rows before this policy is applied. "error" stops with an error and "drop" removes the entire dyad. Conflicting non-missing roles always cause an error. Ignored when no role column is supplied.

Value

A tibble with class dyadMLM_data and metadata about the dyad, member, optional role, and optional time columns.

dyadMLM documentation built on Oct. 28, 2026, 1:06 a.m.