LINEAR_CAP_AND_LAG_MODEL

Python object: phenotypic.schema._linear_cap_and_lag_model.LINEAR_CAP_AND_LAG_MODEL

Fitted parameters of the double-softplus growth model (with saturation).

Output columns are metric-qualified: each header is LinearCapAndLagModel_<metric>_<parameter>, where <metric> records the measurement the model was fit on (self.on with its category prefix stripped, e.g. Shape_Area → Area). For example, fitting on Shape_Area emits LinearCapAndLagModel_Area_v (post-lag growth rate) and LinearCapAndLagModel_Area_s0 (initial size). The labels below are the <parameter> segment; the <metric> infix is filled in at fit time.

Category: LinearCapAndLagModel

Column label	Description	Type
LinearCapAndLagModel_v	The post-lag phase growth rate.	Tier 1 · Direct phenotype
LinearCapAndLagModel_s0	The initial size	Tier 1 · Direct phenotype
LinearCapAndLagModel_lambda	The duration of the lag phase	Tier 1 · Direct phenotype
LinearCapAndLagModel_alpha	lag phase transition sharpness	Tier 2 · Descriptive trait
LinearCapAndLagModel_smax	Carrying capacity used by the model. Either the user-provided scalar or the per-group observed maximum.	Tier 1 · Direct phenotype
LinearCapAndLagModel_beta	Saturation transition sharpness. Fitted per-group when a saturation shoulder is detected and beta is None at construction; held at the user-provided scalar (or the module default) when no shoulder is present.	Tier 2 · Descriptive trait
LinearCapAndLagModel_mode	Fit variant selected per-group: ‘fixed_beta’ (beta held at the user-provided or module-default value) or ‘fitted_beta’ (beta fitted as a 5th free parameter when a saturation shoulder is detected).	Quality