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generalised logistic function : ウィキペディア英語版
generalised logistic function


The generalised logistic function or curve, also known as Richards' curve, originally developed for growth modelling, is an extension of the logistic or sigmoid functions, allowing for more flexible S-shaped curves:
:Y(t) = A + }
where Y = weight, height, size etc., and t = time.
It has five parameters:
*A: the lower asymptote;
*K: the upper asymptote. If A=0 then K is called the carrying capacity;
*B: the growth rate;
*\nu > 0 : affects near which asymptote maximum growth occurs.
*Q: is related to the value Y(0)
*C: typically takes a value of 1.
The equation can also be written:
:Y(t) = A + }
where M can be thought of a starting time, t_0
(at which Y(t_0) = A + )
Including both Q and M can be convenient:
:Y(t) = A + }
this representation simplifies the setting of both a starting time and the value of Y at that time.
The logistic, with maximum growth rate at time M, is the case where Q = \nu = 1.
==Generalised logistic differential equation==
A particular case of the generalised logistic function is:
:Y(t) = }
which is the solution of the so-called Richard's differential equation (RDE):
:Y^(t) = \alpha \left(1 - \left(\frac \right)^ \right)Y
with initial condition
:Y(t_0) = Y_0
where
:Q = -1 + \left(\frac \right)^
provided that ν > 0 and α > 0.
The classical logistic differential equation is a particular case of the above equation, with ν =1, whereas the Gompertz curve can be recovered in the limit \nu \rightarrow 0^+ provided that:
:\alpha = O\left(\frac\right)
In fact, for small ν it is
:Y^(t) = Y r \frac\right) \right)} \approx r Y \ln\left(\frac\right)
The RDE suits to model many growth phenomena, including the growth of tumours. Concerning its applications in oncology, its main biological features are similar to those of Logistic curve model.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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