Read residual check plots

How to read the pages drawn by check_dyad_residuals() and how to follow up flags. plot() redraws a check saved with plot = FALSE.

Usage

# S3 method for class 'dyadMLM_residual_check'
plot(x, ask = NULL, panels = TRUE, ...)

Arguments

x: A result from check_dyad_residuals().
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.
...: Unused.

Value

x, invisibly.

Residual check pages

For each composition, the first page shows uniform QQ and PIT quartiles against predicted outcomes, like DHARMa's main plot. The second shows mean PIT distance. Each supplied predictor gets a page of PIT quartiles.

PIT (probability integral transform) residuals rank each outcome from 0 (low) to 1 (high) relative to its own simulated values. A suitable model should produce ranks roughly evenly spread between 0 and 1, allowing for the variation shown in blue. PIT distance is 2 * abs(PIT - 0.5). Blue envelopes are global: they are built so that the whole red curve of a correct model stays inside in a nominal 95% of datasets. This holds within each panel, not across roles, predictors, or pages. Because the model was fitted to these same data, departures often look smaller than they are, as in the settings studied by Robins et al. (2000) and in our simulations.

Predicted outcomes and numeric predictors with more than eight distinct values are grouped into up to eight bins per role, with at least about 20 observations per bin. As in DHARMa, these axes show ranks. Tick labels give the original values at the minimum, quartiles and maximum. Grey points show the observed PIT residuals. With four or more bins, PIT quartiles are smoothed across neighboring bins, using distances on the original scale. quartile_lines = "qgam" in check_dyad_residuals() replaces these smoothed quartiles with qgam lines. Because simulated datasets reuse the qgam tuning of the observed data, the 95% coverage of these envelopes is approximate. Categories, and predicted outcomes or numeric predictors with up to eight distinct values, get a box per value, as in DHARMa: a red box spans the observed 25th to 75th percentile, with the median in bold. Red points mark each observed quartile on its own blue range (25th left, median centre, 75th right). Flags come from these points, not from the box lines. All PIT quartile panels show full-model residuals, not partial effects. Bins and separate predictors can hide local patterns and interactions.

Views and compositions

Start with everyone pooled (role = NULL), then also inspect meaningful roles, such as gender, even when the model treats partners as exchangeable: role-specific problems show only there. Use predictors of check_dyad_residuals() to examine other measured characteristics. For separate plots and summaries by a person-level categorical characteristic, use that variable as role.

Each composition gets one pooled column for same-role partners, or separate columns for distinct roles. Supplying roles does not change the model's assumptions. Observations whose composition or role is unknown, for example because the partner's row is missing and data was not supplied, are omitted with a warning.

Reading flags

A flag is a red curve or point outside its blue envelope or range, or a red line outside its dashed limits. Each panel alone rarely flags a correct model, but flags add up across panels, roles, and predictor pages, so look for a consistent pattern, not any single flag. The simulation study reports how often correct and mismatched models were flagged.

With about 40 dyads, most mismatches studied were hard to detect, so the absence of flags does not show a good fit. Panels suggest possible problems, not their cause. Neither a flag nor its absence shows whether estimates or standard errors are wrong. After a flag, fit the model the pattern suggests and compare the estimates and standard errors you report. Mismatches showed mainly in these panels of both checks:

Poisson fitted to overdispersed counts: mean PIT distance, number of zeros, QQ.
Extra zeros: QQ and PIT quartiles (the number of zeros less often).
Omitted role means: QQ and PIT quartiles across predicted outcomes, by role.
Different role SDs: response SD and mean PIT distance, by role.
Spread rising with a predictor: largest absolute deviation, mean PIT distance, QQ, and PIT quartiles across that predictor.
Omitted partner effect: PIT quartiles across the partner's predictor (supply it in predictors of check_dyad_residuals()).

References

Robins, J. M., van der Vaart, A., & Ventura, V. (2000). Asymptotic distribution of P values in composite null models. Journal of the American Statistical Association, 95(452), 1143–1156. doi:10.1080/01621459.2000.10474310