Team:Paris/Modeling/BOB/Akaike

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<center><html><div style="color:#275D96; font-size:2em;">Model Comparison</div></html></center>
<center><html><div style="color:#275D96; font-size:2em;">Model Comparison</div></html></center>
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* Using linear equations in a biological system might seem awkards. However, we wanted to check the relevance of this approach. We have been looking for a criterium that would penalize a system that had many parameters, but that would also penalize a system which quadratic error would be too important while fitting experimental values.
* Using linear equations in a biological system might seem awkards. However, we wanted to check the relevance of this approach. We have been looking for a criterium that would penalize a system that had many parameters, but that would also penalize a system which quadratic error would be too important while fitting experimental values.
* Akaike and Schwarz criteria taken from the information theory met our demands quite well :
* Akaike and Schwarz criteria taken from the information theory met our demands quite well :
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* It is remarquable that Akaike criterion and Hurvich and Tsai criterion are alike. AICc is therefore used for small sample size, but converges to AIC as n gets large. Since we will work with 20 points for each experiment, it seemed relevant to present both models. In addition, Schwarz criterion is meant to be more penalizing.
* It is remarquable that Akaike criterion and Hurvich and Tsai criterion are alike. AICc is therefore used for small sample size, but converges to AIC as n gets large. Since we will work with 20 points for each experiment, it seemed relevant to present both models. In addition, Schwarz criterion is meant to be more penalizing.
* As an experiment, we wished to compare the two models presented below :  
* As an experiment, we wished to compare the two models presented below :  
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System#1 : using the linear equations from our BOB approach : [[Image:syste_akaike_1.jpg]]<br>
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'''System#1''' : using the linear equations from our BOB approach :<br>
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System#2 : using classical Hill functions : [[Image:syste_akaike_2.jpg]]<br>
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[[Image:syste_akaike_1.jpg]]<br>
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'''System#2''' : using classical Hill functions :<br>
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[[Image:syste_akaike_2.jpg]]<br>
* We made a set of data out of a noised Hill function. In fact, our data set was made by using the same equations as System#2, but we introduced a normal noise for each point. Thus, System#1 is penalized because its RSS will be greater than that of System#2. Nevertheless, System#2 will be more penalized by its number of parameters.
* We made a set of data out of a noised Hill function. In fact, our data set was made by using the same equations as System#2, but we introduced a normal noise for each point. Thus, System#1 is penalized because its RSS will be greater than that of System#2. Nevertheless, System#2 will be more penalized by its number of parameters.

Revision as of 12:42, 3 September 2008

(Under Construction)

Model Comparison


  • Using linear equations in a biological system might seem awkards. However, we wanted to check the relevance of this approach. We have been looking for a criterium that would penalize a system that had many parameters, but that would also penalize a system which quadratic error would be too important while fitting experimental values.
  • Akaike and Schwarz criteria taken from the information theory met our demands quite well :
Akaike criterion :
AIC.jpg
Hurvich and Tsai criterion :
AICc.jpg
Schwarz criterion :
BIC.jpg

where n denotes the number of experimental values, k the number of parameters and RSS the residual sum of squares. The best fitting model is the one for which those criteria are minimized.

  • It is remarquable that Akaike criterion and Hurvich and Tsai criterion are alike. AICc is therefore used for small sample size, but converges to AIC as n gets large. Since we will work with 20 points for each experiment, it seemed relevant to present both models. In addition, Schwarz criterion is meant to be more penalizing.
  • As an experiment, we wished to compare the two models presented below :

System#1 : using the linear equations from our BOB approach :
Syste akaike 1.jpg
System#2 : using classical Hill functions :
Syste akaike 2.jpg

  • We made a set of data out of a noised Hill function. In fact, our data set was made by using the same equations as System#2, but we introduced a normal noise for each point. Thus, System#1 is penalized because its RSS will be greater than that of System#2. Nevertheless, System#2 will be more penalized by its number of parameters.





We mostly used the definition of the criteria given in : [http://www.liebertonline.com/doi/pdf/10.1089/rej.2006.9.324 K. Kikkawa.Statistical issue of regression analysis on development of an age predictive equation. Rejuvenation research, Volume 9, n°2, 2006.]