feat(psychometric): recover Driver p.16 discreteDIFFUSIONstd after positive asymDIFFUSION - #188
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…sitive asymDIFFUSION Driver, Oud, and Voelkle (2017, p. 16; Eq. 4; footnote 4; §7.1) print standardised matrices with the suffix std when appropriate. Footnote 4 standardises using only the relevant variance, not the total. Process noise is within-subject, so that variance is asymDIFFUSION. Form strictly positive -q/(2a) first, then Q_Δt / p. Unstandardised Q_Δt is defined for growing a ≥ 0 and for zero diffusion; standardised DIFFUSION is not. The continuous standardisation -2a is not discreteDIFFUSIONstd. Q_Δt / (trait + p + added) uses TRAITVAR and is not discreteDIFFUSIONstd. Still not a Kalman filter, not a matrix expm, not ESEM estimation, not DSEM, and not ctsem estimation.
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Folded into the consolidation vehicle #231; this draft stays open until the vehicle merges, then closes as superseded-by-consolidation. |
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Superseded by consolidation: this slice's Driver et al. (2017) standardization recovery landed on protected main through the integration vehicles (#231/#232), and its contract coverage is carried by the reconciled suites (multilevel_event_time_recovery_contract, rubin_and_mean_gate_contract) with test-signature repair tracked in #234. The stacked-draft form is retired to keep the delivery queue at review-ready work only; no capability is lost — the exact-head provenance remains in the vehicle PR descriptions and CHANGELOG. |
Stacked on #187 (
6afd048) / #185 (69ffec6) / #184 (6b93147) / #183 (c10097be) / #182 (2d4d6bf) / #181 (542806b) / #49 (6f95142). ADR 0005 executable slice stays insidepsychometric_core. This is not a second invariance crate and does not recreate #78 or #80.psychometric_corerecovers the Driver, Oud, and Voelkle (2017, p. 16discreteDIFFUSIONstd; Eq. 3–4, pp. 4–5; footnote 4; §7.1, pp. 18–19; JSS PDF re-opened 2026-08-23T13:06Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised discreteDIFFUSION. Page 16 prints discrete-time transformations for a chosen event interval (discreteDRIFT,discreteDIFFUSION) and, when appropriate, standardised matrices with the suffixstd. Footnote 4: standardisations use only the relevant variance, not the total. Process noise is within-subject stochastic input, so that relevant variance is within-subjectasymDIFFUSION-q / (2 a), the same footnote 4 variance used forDRIFT.Form strictly positive
asymDIFFUSIONfirst, thenQ_Δtfrom Equation 4, thenQ_Δt / (-q / (2 a)). In the scalar stationary case that ratio equals1 - exp(2 a Δt).Q_Δtis defined for growinga ≥ 0and for zero diffusion; standardisedDIFFUSIONis not.asymDIFFUSIONhas no positive SD and fails closed (StandardisedDiscreteDiffusionRequiresPositiveWithinSubjectVariance).a ≥ 0cannot form finiteasymDIFFUSIONand fails closed (StationaryVarianceRequiresStableDrift).q / (-q / (2 a)) = -2 ais notdiscreteDIFFUSIONstd.Q_Δt / (trait + p + added)uses the total, notasymDIFFUSION, and is notdiscreteDIFFUSIONstdwhenTRAITVARis nonzero.TRAITVARis not the footnote 4 standardisation variance.Still not a Kalman filter, not a matrix
expm, not ESEM estimation, not DSEM, and not ctsem estimation. Meredith (1993) remains unread (Unpaywall 2026-08-23T13:06Z:is_oa: false; title Measurement Invariance, Factor Analysis and Factorial Invariance). Mislevy (1991, Psychometrika, 56, 177–196) remains unread (Unpaywall 2026-08-23T13:06Z:is_oa: false; title Randomization-Based Inference about Latent Variables from Complex Samples).Do not merge, self-approve, or request Copilot.