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Check outcome distributions against model simulations

Source: R/predictive_checks_outcomes.R
check_dyad_outcomes.Rd

[Experimental] A model should reproduce the distribution of the observed outcomes. This check compares the observed outcome distribution and summaries (response SD after subtracting predictions, largest absolute deviation from prediction, number of zeros) with complete datasets from simulate_dyad_responses(). This helps identify mismatches in the model's assumptions, such as the distribution family, dispersion, or zero inflation. For PIT residual patterns, also run check_dyad_residuals() with the same simulations. For guidance on how to read the plots, see plot.dyadMLM_outcome_check().

Usage

check_dyad_outcomes(
  simulations,
  dyad = NULL,
  role = NULL,
  member = NULL,
  plot = TRUE,
  ask = NULL,
  panels = TRUE,
  data = NULL
)

Arguments

simulations

An object from simulate_dyad_responses(). At least 1,000 simulated datasets are recommended for stable comparisons.

dyad

Dyad ID column, with or without quotes. Required when role is supplied.

role

Role column, with or without quotes, such as gender. NULL (default) pools all observations. Otherwise, each composition (pair of partners' roles, such as female-male) is checked separately. Each member must keep the same role. With few dyads in a composition, the simulated ranges are wide, so its panels lack sensitivity.

member

Person ID column, with or without quotes. Required with role when dyads have repeated observations.

plot

If TRUE (default), draw the comparison plots for visual checks. If FALSE, ask and panels are ignored.

ask

Whether to pause between figures on an interactive device. NULL (default) pauses when there is more than one figure, TRUE pauses and FALSE draws without pausing. File devices never pause.

panels

If TRUE (default), arrange checks in figures by composition, with one column per role or a pooled column. If FALSE, draw each check and role separately.

data

The unchanged data used to fit the model. Supply it when the columns you name (such as role) are not in the model formulas. Also supply it when rows were excluded during fitting (e.g., for missing values). Otherwise, a person whose partner's rows were excluded has an unknown composition and is omitted.

Value

Invisibly returns a dyadMLM_outcome_check list with role-specific summary matrices and outcome distributions in compositions. Matrices have one column per dataset (observed first, then all simulated). Optionally, suppress plotting with plot = FALSE and draw the saved result later with plot(result), which also takes ask and panels.

Scope

Complete simulated datasets keep the fitted partner and time dependence and any modeled differences in variability between roles. Unlike DHARMa's default (since version 0.5.0), simulations redraw all random effects. Dyadic models often fix the residual variance near zero (dispformula = ~ 0) and model the partners' residual variances and covariance as random effects. If held fixed, these random effects would reproduce the data almost exactly. Parameters stay fixed at their fitted values, so parameter uncertainty is not included. These are descriptive checks, not significance tests, and differ from DHARMa's plots and tests (see the distribution-checks article). They do not directly assess serial correlation.

Predicted outcomes are mean predictions with random effects set to zero (see simulate_dyad_responses()). The checks use the same fitted rows and families as simulate_dyad_responses() and weight all observations equally. For cross-sectional data, check partner correlations with check_partner_dependence().

See also

check_dyad_residuals(), check_partner_dependence()

Examples

example_data <- prepare_dyad_data(
  dyads_cross,
  dyad = coupleID,
  member = personID,
  model_types = "none",
  seed = 123
)
# Exchangeable dyads: the two random-effect blocks represent the partners'
# residual variances and covariance.
model <- glmmTMB::glmmTMB(
  closeness ~ gender + provided_support +
    us(1 | coupleID) + us(0 + .member_contrast_arbitrary | coupleID),
  dispformula = ~ 0,
  data = example_data
)
simulations <- simulate_dyad_responses(
  model,
  nsim = 100, # Low for a quick example. Use at least 1,000.
  seed = 123
)
check_dyad_outcomes(
  simulations,
  dyad = coupleID,
  role = gender,
  ask = FALSE
)