expdrift_model: Exponential-drift model with gradient

In mnirs: Muscle Near-Infrared Spectroscopy Processing and Analysis

Exponential-drift model with gradient

Description

Model function of SSexponential_drift(): exponential_drift() plus the partial derivatives for the parameters written as bare symbols in the call (see free_params()), so stats::nls() skips stats::numericDeriv(). The hinge derivatives are one-sided at the drift onset.

Usage

expdrift_model(t, A, B, tau, slope_B, drift_fraction, TD = NULL)

Arguments

t	A numeric vector of the predictor variable (time).
A	A numeric parameter for the starting baseline of the response variable.
B	A numeric parameter for the ending asymptote of the response variable.
tau	A numeric parameter for the time constant (\tau) of the exponential response, in units of the predictor variable t.
slope_B	A numeric parameter for the linear drift rate dx/dt of the secondary phase, in response units per unit of the predictor variable t.
drift_fraction	A numeric fraction of the primary amplitude B - A in ⁠(0.5, 1)⁠ at which the linear drift begins, where the primary response reaches A + drift_fraction * (B - A).
TD	A numeric parameter for the time delay before the onset of the exponential response, in units of the predictor variable t. If NULL (default), a 3-parameter model without time delay is used.

Value

A numeric vector of predicted values with a "gradient" attribute when any parameter is free.

mnirs documentation built on Sept. 13, 2026, 1:06 a.m.