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m Originally the dew point was spelled as two words. In keeping with similar 2-word physical chemistry terms such as the melting point, triple point, etcetera, all instances of "dewpoint" in this article were split into two words.
 
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[[Image:Generalized logistic function A0 K1 B1.5 Q0.5 ν0.5 M0.5.png|thumb|right|A=0, K=1, B=3, Q=ν=0.5, M=0]]
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The '''generalised logistic curve''' or '''function''', also known as '''Richards' curve''' is a widely used and flexible [[sigmoid function]] for growth modelling, extending the well-known [[logistic function]].
 
:<math>Y(t) = A + { K-A \over (1 + Q e^{-B(t - M)}) ^ {1 / \nu} }</math>
 
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;
*&nu;>0 : affects near which asymptote maximum growth occurs.  
*''Q'': depends on the value ''Y''(0)
*''M'': the time of maximum growth if ''Q''=&nu;
 
== Generalised logistic differential equation ==
A particular case of Richard's function is:
 
:<math>Y(t) =  { K \over (1 + Q e^{- \alpha \nu (t - t_0)}) ^ {1 / \nu} }</math>
 
which is the solution of the so-called Richard's differential equation (RDE):
 
:<math>Y^{\prime}(t) = \alpha  \left(1 - \left(\frac{Y}{K} \right)^{\nu} \right)Y </math>
 
with initial condition
 
:<math>Y(t_0) = Y_0 </math>
 
where
 
:<math>Q = -1 + \left(\frac {K}{Y_0} \right)^{\nu}</math>
 
provided that &nu; &gt; 0 and &alpha; &gt; 0.
 
 
The classical logistic differential equation is a particular case of the above equation, with &nu; =1, whereas the [[Gompertz curve]] can be recovered in the limit <math>\nu \rightarrow 0^+</math> provided that:
 
:<math>\alpha = O\left(\frac{1}{\nu}\right)</math>
 
In fact, for small &nu; it is
 
:<math>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) </math>
 
 
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==
When estimating parameters from data, it is often necessary to compute the partial derivatives of the parameters at a given data point ''t'' (see <ref name=fekedulegn1999parameter>{{cite journal|last=Fekedulegn|first=Desta|coauthors=Mairitin P. Mac Siurtain, Jim J. Colbert|title=Parameter Estimation of Nonlinear Growth Models in Forestry|journal=Silva Fennica|year=1999|volume=33|issue=4|pages=327–336|url=http://www.metla.fi/silvafennica/full/sf33/sf334327.pdf|accessdate=2011-05-31}}</ref>):
:<math>
\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}
</math>
 
==See also==
*[[Logistic function]]
*[[Gompertz curve]]
*[[Ludwig von Bertalanffy]]
 
==Citations==
{{reflist}}
 
==References==
* 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
 
 
 
 
[[Category:Curves]]

Latest revision as of 23:15, 3 December 2014

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