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Statistical diagnostics for OSA residuals

Source: R/6-osa_residuals.R
osa_diagnostics.Rd

Computes the Stewart and Monnahan (2025) statistical diagnostics for a set of OSA residuals: the standard deviation of the normalized residuals (SDNR) and the lower/upper tail statistics, each with the 95% interval expected under the standard normal null hypothesis (so departures can be judged objectively rather than by eye). Computed per data source (type x fleet) and overall.

Under a correctly specified model OSA residuals are already iid standard normal, so the SDNR is simply their sample standard deviation. Its null interval follows the chi-square result for the sample standard deviation of n standard normals (Francis 2014). Each tail statistic is the r-th order statistic of the residuals, at index round(p * (n + 1)), and its null interval is exact: the r-th order statistic of n uniforms is Beta(r, n - r + 1). Nothing is simulated.

Usage

osa_diagnostics(osa, nsim = 10000, probs = c(0.025, 0.975), seed = 123)

Arguments

osa

An rceattle_osa object from osa_residuals(), or a data frame with residual and (optionally) type/fleet columns.

nsim

Ignored from 5.29.0, when the tail null intervals became exact; retained so existing calls keep working, and warns when supplied.

probs

Lower/upper tail probabilities. Default c(0.025, 0.975).

seed

Ignored from 5.29.0, as nsim is; the residuals themselves are randomized-quantile, so seed osa_residuals() instead.

Value

A data frame (class "rceattle_osa_diagnostics", so it prints as a compact severity-tagged summary; every column is still there and $ works as before) with one row per data source plus an "all" row, with columns: group (the "<source> fleet <n>" label), source, fleet, n, sdnr, sdnr_lo, sdnr_hi, lower, lower_lo, lower_hi, upper, upper_lo, upper_hi, the order statistic each tail was read at (lower_r, upper_r) and its exact nominal probability r/(n + 1) (lower_p, upper_p, which differ from probs at small n), and the logical flags sdnr_ok, lower_ok, upper_ok (TRUE when the statistic is inside its null interval). On the "all" row source and fleet are NA.

References

Francis, R.I.C.C. 2014. Replacing the multinomial in stock assessment models: a first step. Fish. Res. 151:70-84.

Stewart, I.J., and Monnahan, C.C. 2025. Can. J. Fish. Aquat. Sci. 82:1-13.

See also

osa_residuals()