Book Image

Simulation for Data Science with R

By : Matthias Templ
Book Image

Simulation for Data Science with R

By: Matthias Templ

Overview of this book

Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.
Table of Contents (18 chapters)
Simulation for Data Science with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Design-based simulation


Design-based simulations are particularly important when the selection probabilities for statistical units of a finite sampling frame are not equal, that is, when samples are drawn with a complex sampling design. This primarily relates to any sampling from finite populations, for example, samples drawn from a population register.

The costs of a sample survey can be reduced if the sample is drawn with a certain complex sampling design. For example, for poverty measurement, a household with a single parent and children might be included with a higher probability than households with another composition of household members, because it's likely that the single parent household is poor (basically the target variable).

Tip

Basically, in design-based simulations R samples from a finite population are drawn using a complex sampling design, wherein the population is simulated in a close-to-reality manner.

For each sample, a parameter of the population is estimated and the estimations...