Fit a conditional capacity-recovery curve (legacy function name)

Fits y(t) = B * (1 - A * exp(-r * t)) after a specified disturbance peak. B is fixed from the observed baseline. A is a fractional recovery deficit and r is a trajectory recovery rate. They are NOT the accessibility alpha and reservoir exchange k in the accessible-capacity model.

Tabulates estimates and independently supplied targets. There is no default mapping from recovery-curve amplitude to accessibility or from recovery rate to reservoir exchange kinetics.

Usage

hrri_infer_architecture(
  Eh_stability,
  id = NULL,
  perturb_time = NULL,
  tau_unit = c("day", "hour", "week"),
  group_cols = c("plot", "depth"),
  fit_edc = FALSE,
  min_points = 3L,
  verbose = TRUE,
  control = list(),
  baseline_end = NULL
)

# S3 method for class 'hrri_arch'
print(x, ...)

# S3 method for class 'hrri_arch'
summary(object, ...)

# S3 method for class 'hrri_arch'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

validate_architecture(arch, params = NULL)

# S3 method for class 'hrri_arch_validation'
print(x, ...)

Arguments

Eh_stability: Data frame containing EAC and optionally EDC.
id: Aligned identifiers containing time and grouping columns if absent.
perturb_time: Disturbance peak time; NULL detects the EAC minimum. Detection is descriptive and can select noise or the last observation.
tau_unit: Time unit: day, hour or week.
group_cols: Columns defining one trajectory. Include plant_id when plant-level trajectories are separate; duplicate times are rejected.
fit_edc: Also fit increasing EDC recovery toward its own baseline. This is inappropriate for a decreasing EDC trajectory; default FALSE.
min_points: Minimum distinct post-peak times (at least three). This is a computational threshold, not proof of identifiability.
verbose: Print fit status.
control: Named list passed to stats::nls.control.
baseline_end: Explicit end of the baseline window, strictly before the peak. NULL uses only the first observation time as a stated assumption.
x: An hrri_arch object.
...: Additional method arguments.
object: An hrri_arch object.
row.names, optional: Standard data-frame method arguments.
arch: An hrri_arch object with a keyed ground_truth data frame.
params: Explicit strings of the form estimated_column:true_column.

Value

An hrri_arch object containing estimates, conditional fit status, fit objects and optional summaries of supplied latent columns. Legacy columns Q_eac, alpha_eac, k_eac and M_eac remain for compatibility. Prefer the aliases baseline_eac, deficit_fraction_eac, recovery_rate_eac and terminal_ratio_eac. M_eac is a terminal-to-baseline ratio, not a measure of causal memory. k_eac_h converts the trajectory rate to reciprocal hours, not exchange kinetics.

Data frame with finite_pair flags and descriptive aggregate statistics. The caller must establish that paired columns have the same definition and units.

Details

A nonlinear fit converging does not establish structural or practical identifiability. Baseline uncertainty is not propagated into coefficient SEs. With Cacc(t) = Q * alpha * (1 - exp(-k*t)), Q and alpha cannot be separated using that curve alone. This function does not solve that inverse problem. Fit only a single recovery window. Nonmonotonic and multi-event trajectories require a different model and residual diagnostics.

Examples