recover_exchangeable_covariance: Recover member-level residual covariance from exchangeable...

In dyadMLM: Tools for Dyadic Multilevel Models

Recover member-level residual covariance from exchangeable random-effect blocks

Description

Back-transforms covariance matrices from paired shared and member-difference random-effect blocks to the covariance structure of two exchangeable members. The result is on the fitted random effects' linear-predictor scale. In non-Gaussian models, it therefore describes a Gaussian latent covariance, not response-scale residual covariance. For the model specification, derivation, and interpretation, see the exchangeable APIM vignette.

Usage

recover_exchangeable_covariance(
  model,
  block_pairings = NULL,
  posterior = c("mean", "median", "draws")
)

Arguments

model	A fitted glmmTMB or single-response brmsfit model. For glmmTMB, only conditional-model random effects are processed.
block_pairings	NULL (default) for automatic block matching. Otherwise, supply one block-pair specification or a list of block-pair specifications. Each pair contains: shared_block: a single string naming the shared random-effect term copied from the fitted model formula or an equivalent selector (see Details), or NULL if the entire shared block was omitted when fitting; difference_block: a single string naming the member-difference random-effect term or an equivalent selector, or NULL if the entire difference block was omitted; difference_indicator: the exact name of the difference-indicator column used in difference_block. It is required when difference_block selects a block and optional when difference_block = NULL; shared_indicator: the exact shared composition-indicator column, coded 1 on the rows of this composition and 0 elsewhere, needed only for composition-specific blocks in mixed-dyad models. It defaults to "1", meaning that every fitted row belongs to the pair and an ordinary intercept is the shared intercept coordinate.
posterior	For brms models, return posterior "mean" matrices (default), "median" matrices, or full posterior "draws". Ignored for glmmTMB models.

Details

Automatic matching recognizes the compact .member_contrast_arbitrary and composition-qualified ⁠.member_contrast_*_arbitrary⁠ names. It first looks for the corresponding ⁠.is_*⁠ shared block. It requires the two blocks to use the same grouping factor and the same underlying terms. Most models fitted with dyadMLM-generated columns therefore need only:

result <- dyadMLM::recover_exchangeable_covariance(model)
print(result)

Supply block_pairings when automatic matching is ambiguous or when a model uses custom indicators or deliberately omitted blocks or terms. To specify one pair with a custom difference indicator:

result <- dyadMLM::recover_exchangeable_covariance(
  model,
  block_pairings = list(
    shared_block = "(1 + time | coupleID)",
    difference_block = "(0 + my_diff + I(my_diff * time) | coupleID)",
    difference_indicator = "my_diff"
  )
)

For multiple covariance levels, wrap the pairings in an outer list. For example, in a Gaussian glmmTMB model fitted with dispformula = ~ 0, this call recovers both a stable dyad-level covariance with an omitted difference time slope and the same-occasion partner residual covariance:

result <- dyadMLM::recover_exchangeable_covariance(
  model,
  block_pairings = list(
    dyad = list(
      shared_block = "(1 + diaryday | coupleID)",
      difference_block = "(0 + .member_contrast_arbitrary | coupleID)",
      difference_indicator =
        ".member_contrast_arbitrary"
    ),
    same_occasion = list(
      shared_block = "(1 | coupleID:diaryday)",
      difference_block = "(0 + .member_contrast_arbitrary | coupleID:diaryday)",
      difference_indicator =
        ".member_contrast_arbitrary"
    )
  )
)

At the dyad level, the fitted model includes a shared time slope but no difference time slope. Thus, the two members' time random effects are identical at this level, with correlation +1 whenever the shared slope variance is non-zero; at zero variance, the correlation is undefined. Covariances involving the diary-day slope are therefore supplied entirely by the shared block.

The random-effect terms may be copied exactly from the model formula. Equivalent backend syntax is also recognized, such as (1 + time | group) and us(1 + time | group), or (0 + x || group) and diag(0 + x | group). A custom difference-indicator name is supplied literally. Difference slopes may be written in either interaction order or as a simple product inside I().

When a difference block is supplied and the fitted model frame retains the indicator columns, difference_indicator must assign -1 and +1 to the two arbitrary member positions consistently within each dyad. For composition-specific blocks, it must be zero where shared_indicator is zero.

The function checks whether both positions occur within each supported fitted grouping unit. It rejects coding when no group contains both positions. For custom difference indicators supplied through block_pairings, it also warns when only some groups are one-sided, which can result from fitted-row filtering, and when stable member assignments cannot be verified across repeated rows without a member identifier.

Value

An exchangeable_covariance object: a named list with one element per matched block pairing. Each element contains shared_term and difference_term (each a fitted term string or NULL), plus the member-level variance-covariance matrix in varcov and its standard-deviation/correlation representation in sdcor, with standard deviations on the diagonal and correlations off the diagonal. Covariance dimension labels member_1 and member_2 denote arbitrary exchangeable positions, not substantive roles or encodings. Caller-supplied outer names are preserved; unnamed pairings receive stable names pair_1, pair_2, and so on in resolved order. varcov and sdcor are matrices, except when posterior = "draws" for a brms model, where they are draw by coefficient by coefficient arrays. brms quantities are transformed draw by draw before summarizing.

What omitted blocks and terms mean

recover_exchangeable_covariance() only describes constraints that were already imposed when the model was fitted. It does not remove a block, set a variance to zero, or otherwise constrain the supplied model. Describe only the structure that was actually fitted.

If a term occurs in only one selected block, the function represents the missing coordinate as a structural zero:

• A term present only in the shared block has no difference component, so the two members have identical random effects for that term.

• A term present only in the difference block has no shared component, so the two members have equal-magnitude, opposite-sign random effects for that term.

Setting difference_block = NULL or shared_block = NULL applies the corresponding rule to the entire omitted block. This is valid only when that block is truly absent from the fitted model. Do not use NULL merely to ignore an existing block; the resulting back-transformation would be incorrect.

See the constrained-block example in the exchangeable APIM vignette.

Backend note

For glmmTMB, random effects in non-conditional components are ignored with a warning. See the vignette's extension to exchangeable random slopes section for the manual calculation.

In brms, cross-sectional and same-occasion partner dependence can be represented directly with unstr(time = member_position, gr = residual_group). With Gaussian outcomes, sigma ~ 1 supplies the common residual scale. For families such as Poisson and negative binomial, unstr() adds a latent residual scale and correlation on the linear-predictor scale. Here, member_position identifies the same two arbitrary positions within every group, and residual_group identifies dyads in cross-sectional data or dyad-occasions in longitudinal data. This direct specification applies when one covariance structure is sufficient. Separate composition-specific unstr() structures for mixed dyad types are not currently supported in a standard single-response brms model. For Gaussian mixed-dyad residual covariance, use glmmTMB. Shared/difference blocks remain relevant for higher-level random effects and can represent latent link-scale covariance in non-Gaussian models.

See Also

The exchangeable APIM vignette for the model specification, covariance derivation, and constrained-block example. Run vignette("apim", package = "dyadMLM") to open the installed version.

Examples

if (requireNamespace("glmmTMB", quietly = TRUE)) {
  example_data <- prepare_dyad_data(
    dyads_cross,
    dyad = coupleID,
    member = personID,
    role = gender,
    model_types = "none",
    # All three observed compositions in `dyads_cross` are detected and retained
    # by default. This example focuses on `female-female` dyads, so we restrict
    # the analysis here.
    keep_compositions = "female-female",
    seed = 123
  )

  model <- glmmTMB::glmmTMB(
    closeness ~ 1 +
      us(1 | coupleID) +
      us(0 + .member_contrast_arbitrary | coupleID),
    dispformula = ~ 0,
    data = example_data
  )

  recover_exchangeable_covariance(model)
}

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