Generalised logistic function

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A=0, K=1, B=3, Q=ν=0.5, M=0

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 + { K-A \over (1 + Q e^{-B(t - M)}) ^ {1 / \nu} }

where Y = weight, height, size etc., and t = time.

It has six parameters:

  • A: the lower asymptote;
  • K: the upper asymptote. If A=0 then K is called the carrying capacity;
  • B: the growth rate;
  • ν>0 : affects near which asymptote maximum growth occurs.
  • Q: depends on the value Y(0)
  • M: the time of maximum growth if Q

Generalised logistic differential equation[edit]

A particular case of Richard's function is:

Y(t) =  { K \over (1 + Q e^{- \alpha \nu (t - t_0)}) ^ {1 / \nu} }

which is the solution of the so-called Richard's differential equation (RDE):

Y^{\prime}(t) = \alpha  \left(1 - \left(\frac{Y}{K} \right)^{\nu} \right)Y

with initial condition

Y(t_0) = Y_0

where

Q = -1 + \left(\frac {K}{Y_0} \right)^{\nu}

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{1}{\nu}\right)

In fact, for small ν it is

Y^{\prime}(t)  = Y r \frac{1-\exp\left(\nu \ln\left(\frac{Y}{K}\right) \right)}{\nu} \approx r Y \ln\left(\frac{Y}{K}\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.

Gradient[edit]

When estimating parameters from data, it is often necessary to compute the partial derivatives of the parameters at a given data point t (see [1]):


\begin{align}
\frac{\partial Y}{\partial A} &= 1 - (1 + Qe^{-B(t-M)})^{-1/\nu}\\
\frac{\partial Y}{\partial K} &= (1 + Qe^{-B(t-M)})^{-1/\nu}\\
\frac{\partial Y}{\partial B} &= \frac{(K-A)(t-M)Qe^{-B(t-M)}}{\nu(1 + Qe^{-B(t-M)})^{\frac{1}{\nu}+1}}\\
\frac{\partial Y}{\partial \nu} &= \frac{(K-A)\ln(1 + Qe^{-B(t-M)})}{\nu^2(1 + Qe^{-B(t-M)})^{\frac{1}{\nu}}}\\
\frac{\partial Y}{\partial Q} &= -\frac{(K-A)e^{-B(t-M)}}{\nu(1 + Qe^{-B(t-M)})^{\frac{1}{\nu}+1}}\\
\frac{\partial Y}{\partial M} &= -\frac{(K-A)Be^{-B(t-M)}}{\nu(1 + Qe^{-B(t-M)})^{\frac{1}{\nu}+1}}
\end{align}

See also[edit]

Citations[edit]

  1. ^ Fekedulegn, Desta; Mairitin P. Mac Siurtain, Jim J. Colbert (1999). "Parameter Estimation of Nonlinear Growth Models in Forestry". Silva Fennica 33 (4): 327–336. Retrieved 2011-05-31. 

References[edit]

  • Richards, F.J. 1959 A flexible growth function for empirical use. J. Exp. Bot. 10: 290-300.
  • Pella JS and PK Tomlinson. 1969. A generalised stock-production model. Bull. IATTC 13: 421-496.
  • Lei, Y.C. and Zhang, S.Y. 2004. Features and Partial Derivatives of Bertalanffy-Richards Growth Model in Forestry. Nonlinear Analysis: Modelling and Control, Vol 9, No. 1:65-73