Simulation for Data Science with R

Harness actionable insights from your data with computational statistics and simulations using R

Simulation for Data Science with R

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Matthias Templ

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Harness actionable insights from your data with computational statistics and simulations using R
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Book Details

ISBN 139781785881169
Paperback398 pages

Book Description

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

Chapter 1: Introduction
What is simulation and where is it applied?
Why use simulation?
Simulation and big data
Choosing the right simulation technique
Summary
References
Chapter 2: R and High-Performance Computing
The R statistical environment
Generic functions, methods, and classes
Data manipulation in R
High performance computing
Visualizing information
References
Chapter 3: The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions
Machine numbers and rounding problems
Condition of problems
Summary
References
Chapter 4: Simulation of Random Numbers
Real random numbers
Simulating pseudo random numbers
Simulation of non-uniform distributed random variables
Tests for random numbers
Summary
References
Chapter 5: Monte Carlo Methods for Optimization Problems
Numerical optimization
Dealing with stochastic optimization
Summary
References
Chapter 6: Probability Theory Shown by Simulation
Some basics on probability theory
Probability distributions
Winning the lottery
The weak law on large numbers
The central limit theorem
Properties of estimators
Summary
References
Chapter 7: Resampling Methods
The bootstrap
Estimation of standard errors with bootstrapping
The parametric bootstrap
Estimating bias with bootstrap
The jackknife
Cross-validation
Summary
References
Chapter 8: Applications of Resampling Methods and Monte Carlo Tests
The bootstrap in regression analysis
Proper variance estimation with missing values
Bootstrapping in time series
Bootstrapping in the case of complex sampling designs
Monte Carlo tests
Summary
Chapter 9: The EM Algorithm
The basic EM algorithm
The EM algorithm by example of k-means clustering
The EM algorithm for the imputation of missing values
Summary
References
Chapter 10: Simulation with Complex Data
Different kinds of simulation and software
Simulating data using complex models
Model-based simulation studies
Design-based simulation
Inserting missing values
Summary
Chapter 11: System Dynamics and Agent-Based Models
Agent-based models
Dynamics in love and hate
Dynamic systems in ecological modeling
Summary
References

What You Will Learn

  • The book aims to explore advanced R features to simulate data to extract insights from your data.
  • Get to know the advanced features of R including high-performance computing and advanced data manipulation
  • See random number simulation used to simulate distributions, data sets, and populations
  • Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations
  • Applications to design statistical solutions with R for solving scientific and real world problems
  • Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.

Authors

Table of Contents

Chapter 1: Introduction
What is simulation and where is it applied?
Why use simulation?
Simulation and big data
Choosing the right simulation technique
Summary
References
Chapter 2: R and High-Performance Computing
The R statistical environment
Generic functions, methods, and classes
Data manipulation in R
High performance computing
Visualizing information
References
Chapter 3: The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions
Machine numbers and rounding problems
Condition of problems
Summary
References
Chapter 4: Simulation of Random Numbers
Real random numbers
Simulating pseudo random numbers
Simulation of non-uniform distributed random variables
Tests for random numbers
Summary
References
Chapter 5: Monte Carlo Methods for Optimization Problems
Numerical optimization
Dealing with stochastic optimization
Summary
References
Chapter 6: Probability Theory Shown by Simulation
Some basics on probability theory
Probability distributions
Winning the lottery
The weak law on large numbers
The central limit theorem
Properties of estimators
Summary
References
Chapter 7: Resampling Methods
The bootstrap
Estimation of standard errors with bootstrapping
The parametric bootstrap
Estimating bias with bootstrap
The jackknife
Cross-validation
Summary
References
Chapter 8: Applications of Resampling Methods and Monte Carlo Tests
The bootstrap in regression analysis
Proper variance estimation with missing values
Bootstrapping in time series
Bootstrapping in the case of complex sampling designs
Monte Carlo tests
Summary
Chapter 9: The EM Algorithm
The basic EM algorithm
The EM algorithm by example of k-means clustering
The EM algorithm for the imputation of missing values
Summary
References
Chapter 10: Simulation with Complex Data
Different kinds of simulation and software
Simulating data using complex models
Model-based simulation studies
Design-based simulation
Inserting missing values
Summary
Chapter 11: System Dynamics and Agent-Based Models
Agent-based models
Dynamics in love and hate
Dynamic systems in ecological modeling
Summary
References

Book Details

ISBN 139781785881169
Paperback398 pages
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