influ2

influ2 calculates and visualises how explanatory variables, random effects, and spatial fields influence standardised fisheries CPUE indices.

The package uses one model-neutral S3 interface:

diagnostic <- influ(model, focus = "year")
diagnostic
summary(diagnostic)
plot(diagnostic, type = "influence")
plot(diagnostic, type = "cdi", term = "vessel")

Initial adapters are available for:

• stats::glm();
• mgcv::gam();
• brms::brm();
• glmmTMB::glmmTMB();
• sdmTMB::sdmTMB(); and
• tinyVAST::tinyVAST().

The result is a compact influ_diag object with consistent tables and plots across backends. brms posterior coefficients are projected directly into focus-by-term diagnostics, avoiding observation-by-draw-by-term arrays.

# Full posterior calculation, compact stored summaries.
diagnostic <- influ(brms_fit, focus = "year", retain = "summary")

# Retain only draws of the derived diagnostics.
diagnostic <- influ(
  brms_fit,
  focus = "year",
  retain = "derived_draws"
)

# Fast posterior-mean preview.
preview <- influ(brms_fit, focus = "year", uncertainty = "none")

Supported base families in the first implementation are Gaussian, binomial, Poisson, negative binomial, lognormal, Gamma, and Tweedie. Hurdle/delta and zero-inflated components are kept explicit.

Year-effect indices from influ() require an unambiguous focus effect. Offset/exposure diagnostics currently support single-component log-link ratios and identity-link contrasts, not combined or nonlinear-probability outputs. Lognormal support follows each backend's parameterisation; see the main vignette for the supported location, scale, and link combinations.

For an assessment-ready expected-response table, use cpue_index() with an explicit common reference profile or population. Both "standardised" and "standardized" are accepted. GLM, GAM, glmmTMB, complete brms, sdmTMB, and univariate tinyVAST fits are supported for response standardisation. integrate_index() separately calculates area-weighted totals for these same six backends, whether or not the fitted model includes spatial effects.

index <- cpue_index(model, year = "year", reference_data = reference_profile)
as.data.frame(index)  # Year, Mean, Median, SD, CV, Qlower, Qupper, and metadata.
plot_index(index)
geo_mean(c(1, 4, 16))

total <- integrate_index(model, reference_data = prediction_grid,
  area = "area_km2", year = "year",
  area_units = "km^2", response_units = "kg/km^2", units = "kg")
plot_index(total)

These response indices are distinct from the year-effect contrasts used in the step plots. See the CPUE indices article for reference choices, compact uncertainty, and comparison examples. Area integration requires a stated domain and compatible response/area units; integrating CPUE does not automatically turn it into absolute biomass.

Residual diagnostics

Residual overviews also adapt to the response: Bernoulli encounter models use fixed-bin probability calibration, while positive and combined catch responses retain the observed-versus-simulated CDF. No model is refitted by these calls.

checks <- influ_residuals(model, year = "year")
plot(checks)                          # Automatic fourth panel.
plot(checks, type = "distribution")  # Explicit original CDF.

See Residual diagnostics for predictive-envelope interpretation and grouped checks that can reveal missing structure even when pooled encounter calibration looks good.

Installation

pak::pak("quantifish/influ2")

See vignette("influ2") for the framework, memory strategy, and model examples. The separate vignette("bentley-validation") documents and plots the frozen agreement check against the original Bentley implementation.