Book Image

R Statistics Cookbook

By : Francisco Juretig
2 (2)
Book Image

R Statistics Cookbook

2 (2)
By: Francisco Juretig

Overview of this book

R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools. You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making. By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.
Table of Contents (12 chapters)

Nonlinear mixed effects models

Linear mixed effects models assume that a linear relationship exists between the predictors and the target variable. In many cases, this is a problematic assumption; whenever the target is expected to show any kind of saturation effect or have an exponential response with respect to any of the regressors, the linearity assumption needs to be removed.

In medicine and biology, this is usually the case, as dose response studies almost always exhibit a certain kind of saturation effect. The same happens for marketing studies, because spending increasing amounts of resources in order to drive sales up might be effective, but it won’t be effective if that spend is too large.

Fitting nonlinear mixed effects models is much harder than their linear counterpart. Here, we can’t rely on any matrix techniques and we need to attack the problem...