Introduction

Regression analysis is one of the cornerstones of statistics, data science, and analytics. For decades, linear regression has been the first tool analysts reach for when trying to understand relationships between variables. Linear models are easy to interpret, computationally efficient, and often surprisingly effective. However, the real world is rarely linear. Growth processes saturate, decay curves flatten, biological reactions plateau, and financial returns accelerate or slow down in nonlinear ways. In such situations, forcing a linear model onto nonlinear data leads to poor predictions and misleading conclusions.

Nonlinear regression addresses this limitation by allowing the relationship between variables to take curved, exponential, logistic, or other nonlinear forms. In R, nonlinear regression is commonly implemented using the nonlinear least squares approach through the nls() function. This article builds on a practical R-based explanation of nonlinear regression and expands it by discussing its origins, theoretical foundation, real-life applications, and detailed case studies. The goal is to provide a complete, end-to-end understanding suitable for analysts, data scientists, and researchers.

Origins of Nonlinear Regression

The roots of nonlinear regression can be traced back to the development of least squares methods in the early 19th century. The method of least squares was independently developed by Carl Friedrich Gauss and Adrien-Marie Legendre to solve astronomical problems, such as estimating planetary orbits from noisy observations. These early applications already hinted at nonlinear relationships, because orbital motion follows nonlinear physical laws.

As science and engineering advanced, researchers realized that many natural phenomena could not be accurately described using straight-line relationships. Exponential growth models emerged in population studies, logarithmic models appeared in psychophysics, and saturation curves became common in chemistry and biology. By the mid-20th century, nonlinear regression became a standard analytical tool in fields such as pharmacokinetics, enzyme kinetics, and systems engineering.

With the rise of computers, iterative numerical methods made it feasible to estimate nonlinear models efficiently. Modern statistical software, including R, builds on this legacy by providing robust algorithms to fit nonlinear models using least squares optimization.

Linear vs Nonlinear Regression

Linear regression assumes that the dependent variable can be expressed as a linear combination of independent variables and coefficients. Even when transformations are applied, the model remains linear in its parameters. Nonlinear regression, in contrast, allows the parameters themselves to appear inside nonlinear functions such as exponentials, logarithms, or ratios.

For example, an exponential growth model of the form:

y = a * exp(b * x)

is nonlinear in its parameters because the coefficient b appears in the exponent. Such a model cannot be estimated using ordinary linear regression without approximation or transformation. Nonlinear least squares directly estimates the parameters by minimizing the sum of squared differences between observed and predicted values.

*Nonlinear Least Squares in R * R provides the nls() function for fitting nonlinear regression models using nonlinear least squares. Conceptually, nls() works by:

Taking a user-specified nonlinear model formula.

Using initial starting values for the model parameters.

Iteratively adjusting the parameters to minimize the residual sum of squares.

Unlike linear regression, nonlinear regression requires the analyst to specify the functional form of the relationship. This makes domain knowledge and exploratory data analysis essential steps before model fitting.

A simple illustration involves fitting an exponential curve to data generated from an exponential process. When a linear model is applied to such data, the fitted line fails to capture the curvature. In contrast, a nonlinear model using nls() closely follows the underlying pattern and yields significantly lower prediction error. This demonstrates why nonlinear regression is indispensable when the data-generating process itself is nonlinear.

Importance of Starting Values

One of the most critical aspects of nonlinear regression is the choice of starting values for model parameters. The optimization algorithms used by nls() are iterative and rely on reasonable initial guesses to converge to a meaningful solution. Poor starting values can lead to slow convergence, convergence to local minima, or complete model failure.

In practice, starting values are often chosen by visually inspecting the data, using theoretical knowledge, or fitting simpler approximations. For instance, in an exponential model, the intercept parameter can be approximated from the initial value of the response variable, while the growth rate can be inferred from how quickly the curve rises.

Self-Starting Functions in R

To reduce the burden of manually specifying starting values, R provides self-starting nonlinear models. These functions automatically estimate reasonable initial parameter values based on the data. They are particularly useful for analysts who are new to nonlinear modelling or working with well-known functional forms.

Examples of self-starting models in R include logistic growth models, Weibull curves, Gompertz growth functions, and Michaelis–Menten kinetics. These functions encapsulate decades of domain knowledge and statistical practice, allowing users to focus on interpretation rather than numerical details.

**Real-Life Applications of Nonlinear Regression

1. Biology and Medicine**

Nonlinear regression is widely used in biology to model enzyme kinetics, drug absorption, and dose–response relationships. The Michaelis–Menten model, for example, describes how reaction rate depends on substrate concentration. Such models help researchers estimate critical parameters like maximum reaction rate and affinity constants, which are essential for drug development and biochemical analysis.

2. Economics and Finance

In economics, nonlinear models capture diminishing returns, learning curves, and market saturation. Financial analysts use nonlinear regression to model compound interest, option pricing dynamics, and volatility clustering. Linear models often underestimate risks in such nonlinear systems.

3. Engineering and Physics

Engineering systems frequently exhibit nonlinear behaviour due to friction, saturation, and feedback loops. Nonlinear regression helps estimate parameters in stress–strain relationships, signal decay, and control system responses.

4. Marketing and Growth Analytics

Customer acquisition, product adoption, and revenue growth often follow S-shaped curves. Logistic and Gompertz models are commonly used to forecast growth and identify saturation points. Nonlinear regression enables businesses to make realistic long-term projections.

Case Study 1: Exponential Growth Modelling

Consider a dataset where the response variable grows exponentially with respect to an input variable. A linear regression model fails to capture the accelerating trend, resulting in high residual error. By fitting a nonlinear exponential model using nls(), the analyst obtains a curve that closely follows the observed data.

Comparing root mean squared error values between linear and nonlinear models clearly shows the superiority of the nonlinear approach. This case study highlights how nonlinear regression improves both interpretability and predictive accuracy when the underlying process is exponential.

Case Study 2: Enzyme Kinetics Using Michaelis–Menten Models

A classic dataset involving enzymatic reactions demonstrates the power of nonlinear regression in practice. Reaction rate depends nonlinearly on substrate concentration and approaches a maximum value as concentration increases. By fitting Michaelis–Menten models to treated and untreated samples, analysts can compare kinetic parameters across experimental conditions.

Using self-starting functions simplifies the modelling process and produces parameter estimates that match those obtained with manually specified starting values. This case study illustrates how nonlinear regression supports scientific inference and experimental comparison.

Evaluating Model Fit

Goodness of fit in nonlinear regression is assessed using metrics similar to those in linear regression, including residual analysis and correlation between observed and predicted values. High correlation and low residual error indicate that the model captures the underlying relationship effectively.

However, analysts should also examine residual plots and consider overfitting, especially when using complex nonlinear models. A simpler model with slightly higher error may be preferable if it generalizes better to new data.

Strengths and Limitations

Nonlinear regression provides a parametric framework that directly reflects theoretical relationships between variables. This interpretability is a major advantage over black-box models such as decision trees or neural networks. However, nonlinear regression becomes challenging when models are highly complex or when the functional form is unknown.

In such cases, machine learning methods may outperform nonlinear regression in prediction tasks, but they often sacrifice interpretability. Choosing the right approach depends on the problem context and analytical goals.

Conclusion

Nonlinear least squares and nonlinear regression are essential tools for analysing real-world data that does not conform to linear assumptions. Rooted in centuries of mathematical and scientific development, these methods remain highly relevant in modern analytics. R’s nls() function and its self-starting variants provide a flexible and powerful framework for fitting nonlinear models across diverse domains.

By understanding the origins, applications, and practical considerations of nonlinear regression, analysts can make more accurate models, draw meaningful insights, and bridge the gap between theory and real-world data. Nonlinear regression is not merely an alternative to linear models; it is a necessity for capturing the true complexity of many natural and human-made systems.

This article was originally published on Perceptive Analytics. At Perceptive Analytics our mission is “to enable businesses to unlock value in data.” For over 20 years, we’ve partnered with more than 100 clients—from Fortune 500 companies to mid-sized firms—to solve complex data analytics challenges. Our services include Power BI Consulting Services in San Francisco, Power BI Consulting Services in San Jose, and Power BI Consulting Services in Seattle turning data into strategic insight. We would love to talk to you. Do reach out to us. T