1. Non-Linear Regression
What is it?
Why and when to use?
Model assumptions
Typical non-linear models
Obtaining parameter estimates
7-Apr-19
AGR206

2. Non-linear regression
What is it?
Model is non-linear in the parameters.
Types of models:
Linear.
Intrinsically linear.
Intrinsically non-linear.
Non-linear regression minimizes SSE numerically, by trying many values of the parameter estimates.
Properties of parameters and predictions cannot be derived with equations.
7-Apr-19
AGR206

3. When and why to use?
Function relating Y to X's is known on the basis of a mechanistic understanding of the process.
For example, the logistic growth model is based on the fact that for some populations, individuals compete for resources and reduce each other's growth rate linearly as density increases.
Parameters of the model have a direct biological meaning. The model may offer the only way to empirically estimate the value of the parameter.
In the previous example, the parameters of the logistic equations are the intrinsic relative growth rate (r) and carrying capacity (K).
Nonlinear models may characterize responses better with fewer parameters than linear ones, even when no apriori functional form is available.
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AGR206

4. Exponential growth
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5. Two-term exponential
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6. Mitscherlich’s response
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7. Mitscherlich graph
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8. Michaelis-Menten
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9. Segmented Model
7-Apr-19
AGR206