# Lesson 19: Non-linear Models

Lesson 19: Non-linear Models

## Overview

Here we'll explore the logistic regression model and a function for least-squares fitting of arbitrarty non-linear functions.

## Objectives

Upon completion of this lesson, you should be able to:

• Recognize when a logistic model would be appropriate
• Fit a logistic model to proportion (group) data
• Fit a logistic model to binary (individual) data
• Interpret coefficients of the logistic model
• Fit arbitrary non-linear models using nls()

## Data and R Code Files

The R code file and data files for this lesson can be found on the Essential R - Notes on learning R page.

# 19.1 - A Brief Definition of the Logistic Model

19.1 - A Brief Definition of the Logistic Model

Here we'll discuss the baxic background assumptions of the Logistic regression model.

# 19.2 - Fitting a Logistic Model

19.2 - Fitting a Logistic Model

In this video we'll examine a data set which shows group proportions (so any value between 0 and 1 is possible) and fit a logistic regression to the data.

# 19.3 - Interpreting the Coefficients of the Logistic Model I

19.3 - Interpreting the Coefficients of the Logistic Model I

Now we'll interpret the coefficients of the model we fit in the last video.

# 19.4 - Interpreting the Coefficients of the Logistic Model II

19.4 - Interpreting the Coefficients of the Logistic Model II

In this video we'll plot our regression model over the data and add our midpoint as calculated from the coefficients.

# 19.5 - Logistic Regression on Individual Data I

19.5 - Logistic Regression on Individual Data I

We'll try a different data set now, where each case is binary (either "a" or "b"), but we'll see we can still fit a logistic regression.

# 19.6 - Logistic Regression on Individual Data II

19.6 - Logistic Regression on Individual Data II

We'll wind up our discussion of logistic regression by examining the model we fit in the last video.

# 19.7 - Other Non-linear Models Using nls()

19.7 - Other Non-linear Models Using nls()

Now we can move on to introduce nls() for fitting "non-linear least squares" models. We'll demonstrate with some data for enzyme kinetics that exhibit Michaelis-Menten dynamics.

# 19.8 - Interpreting an nls() Model

19.8 - Interpreting an nls() Model

Here we'll examine the object created by nls().

# 19.9 - Using anova() on nls() Models

19.9 - Using anova() on nls() Models

Finally we'll demonstrate the use of anova() for comparison of nested nls() models.

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