Nonlinear regression: Difference between revisions

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{{Short description|Regression analysis}}

{{Regression bar}}

[[Image:Michaelis-Menten saturation curve of an enzyme reaction.svg|thumb|300 px| See [[Michaelis–Menten kinetics]] for details ]]

In statistics, '''nonlinear regression''' is a form of [[regression analysis]] in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations (iterations).

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In nonlinear regression, a [[statistical model]] of the form,

:<math display="block"> \mathbf{y} \sim f(\mathbf{x}, \boldsymbol\beta)</math>

relates a vector of [[independent variables]], <math>\mathbf{x}</math>, and its associated observed [[dependent variables]], <math>\mathbf{y}</math>. The function <math>f</math> is nonlinear in the components of the vector of parameters <math>\beta</math>, but otherwise arbitrary. For example, the [[Michaelis–Menten]] model for enzyme kinetics has two parameters and one independent variable, related by <math>f</math> by:{{efn|This model can also be expressed in the conventional biological notation: <math display="block"> v = \frac{V_\max\ [\mathrm{S}]}{K_m + [\mathrm{S}]} </math>}}

:<math display="block"> v f(x,\boldsymbol\beta)= \frac{V_\max\beta_1 [\mbox{S}]x}{K_m\beta_2 + [\mbox{S}]x} </math>

}}

This function, which is a rectangular hyperbola, is ''{{em|nonlinear''}} because it cannot be expressed as a [[linear combination]] of the two ''<math>\beta</math>''s.

:<math> f(x,\boldsymbol\beta)= \frac{\beta_1 x}{\beta_2 + x} </math>

This function, which is a rectangular hyperbola, is ''nonlinear'' because it cannot be expressed as a [[linear combination]] of the two ''<math>\beta</math>''s.

[[Systematic error]] may be present in the independent variables but its treatment is outside the scope of regression analysis. If the independent variables are not error-free, this is an [[errors-in-variables model]], also outside this scope.

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The assumption underlying this procedure is that the model can be approximated by a linear function, namely a first-order [[Taylor series]]:

:<math display="block"> f(x_i,\boldsymbol\beta) \approx f(x_i,0) + \sum_j J_{ij} \beta_j </math>

where <math>J_{ij} = \frac{\partial f(x_i,\boldsymbol\beta)}{\partial \beta_j}</math> are Jacobian matrix elements. It follows from this that the least squares estimators are given by

:<math display="block">\hat{\boldsymbol{\beta}} \approx \mathbf { (J^TJ)^{-1}J^Ty},</math>

compare [[generalized least squares]] with covariance matrix proportional to the unit matrix. The nonlinear regression statistics are computed and used as in linear regression statistics, but using '''J''' in place of '''X''' in the formulas.

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==Ordinary and weighted least squares==

The best-fit curve is often assumed to be that which minimizes the sum of squared [[errors and residuals in statistics|residuals]]. This is the [[ordinary least squares]] (OLS) approach. However, in cases where the dependent variable does not have constant variance, or there are some outliers, a sum of weighted squared residuals may be minimized; see [[weighted least squares]]. Each weight should ideally be equal to the reciprocal of the variance of the observation, or the reciprocal of the dependent variable to some power in the outlier case ,<ref>{{cite journal | last1 = Motulsky | first1 = H.J. | last2 = Ransnas | first2 = L.A. | title = Fitting curves to data using nonlinear regression: a practical and nonmathematical review | journal = The FASEB Journal | volume = 1 | issue = 5 | pages = 365–374 | year = 1987 | doi = 10.1096/fasebj.1.5.3315805 | doi-access = free | pmid = 3315805 }}</ref> but weights may be recomputed on each iteration, in an iteratively weighted least squares algorithm.

| last1 = Motulsky

| first1 = H.J.

| last2 = Ransnas

| first2 = L.A.

| title = Fitting curves to data using nonlinear regression: a practical and nonmathematical review

| journal = The FASEB Journal

| volume = 1

| issue = 5

| pages = 365–374

| year = 1987

| doi = 10.1096/fasebj.1.5.3315805

| doi-access = free

| pmid = 3315805

}}</ref>

, but weights may be recomputed on each iteration, in an iteratively weighted least squares algorithm.

==Linearization==

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For example, consider the nonlinear regression problem

:<math display="block"> y = a e^{b x}U \,\!</math>

with parameters ''a'' and ''b'' and with multiplicative error term ''U''. If we take the logarithm of both sides, this becomes

:<math display="block"> \ln{(y)} = \ln{(a)} + b x + u, \,\!</math>

where ''u'' = ln(''U''), suggesting estimation of the unknown parameters by a linear regression of ln(''y'') on ''x'', a computation that does not require iterative optimization. However, use of a nonlinear transformation requires caution. The influences of the data values will change, as will the error structure of the model and the interpretation of any inferential results. These may not be desired effects. On the other hand, depending on what the largest source of error is, a nonlinear transformation may distribute the errors in a Gaussian fashion, so the choice to perform a nonlinear transformation must be informed by modeling considerations.

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For [[Michaelis–Menten kinetics]], the linear [[Lineweaver–Burk plot]]

:<math display="block"> \frac{1}{v} = \frac{1}{V_\max} + \frac{K_m}{V_{\max}[S]}</math>

of 1/''v'' against 1/[''S''] has been much used. However, since it is very sensitive to data error and is strongly biased toward fitting the data in a particular range of the independent variable, [''S''], its use is strongly discouraged.

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* [[Multi expression programming]]

* [[Linear_least_squares#Alternative_formulations|Linear or quadratic template fit]]

==References==

{{Reflist}}

==Notes==

{{notelist}}

==References==

{{Reflist}}

==Further reading==