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Related Experiment Videos

Reducing bias in estimates of the richards growth function shape parameter.

D A McCallum1, P M Dixon

  • 1Savannah River Ecology Laboratory, Aiken, SC 29802.

Growth, Development, and Aging : GDA
|January 1, 1990
PubMed
Summary

A common growth model systematically underestimates the Richards function shape parameter (m). Bias decreases with more frequent sampling during rapid growth, and a simple correction effectively removes this underestimation for accurate growth analysis.

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Area of Science:

  • Ecology
  • Biostatistics
  • Mathematical Biology

Background:

  • The White and Brisbin (1980) process error model is widely used for Richards sigmoid growth function parameter estimation.
  • The Richards function incorporates a shape parameter (m) that generalizes common growth models like logistic and Gompertz.

Purpose of the Study:

  • To evaluate the accuracy of the White and Brisbin (1980) model in estimating the Richards function shape parameter (m).
  • To identify factors influencing bias in parameter estimation and propose a correction method.

Main Methods:

  • Analysis of calibration runs and simulations using the White and Brisbin (1980) process error model.
  • Investigation of bias under varying sampling frequencies and true shape parameter (m) values.

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Main Results:

  • The White and Brisbin (1980) model systematically underestimates the shape parameter (m).
  • Bias decreased with increased sampling frequency during rapid growth and with smaller true values of m.
  • A simple correction method effectively reduced bias to negligible levels for both deterministic and stochastic models.

Conclusions:

  • The commonly used process error model introduces systematic bias in Richards function shape parameter estimation.
  • Adjusting sampling frequency and applying a simple correction are crucial for accurate growth modeling.
  • Estimated asymptote (A) and time (T) parameters showed minimal bias across various conditions.