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2019-R语言新书 Advanced R Statistical Programming and Data Models_ Analysis, Machine Learning, and Visualization-Apress (2019),R语言中2019年新出来的书!!非常的好
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Advanced R
Statistical
Programming
and Data Models
Analysis, Machine Learning, and Visualization
—
Matt Wiley
Joshua F. Wiley

Matt Wiley
Joshua F. Wiley
Advanced R Statistical
Programming and
Data Models
Analysis, Machine Learning,
and Visualization

Advanced R Statistical Programming and Data Models: Analysis, Machine Learning,
and Visualization
ISBN-13 (pbk): 978-1-4842-2871-5 ISBN-13 (electronic): 978-1-4842-2872-2
https://doi.org/10.1007/978-1-4842-2872-2
Library of Congress Control Number: 2019932986
Copyright © 2019 by Matt Wiley and Joshua F. Wiley
is work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the
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Printed on acid-free paper
MattWiley
Columbia City, IN, USA
JoshuaF.Wiley
Columbia City, IN, USA

iii
Table of Contents
Chapter 1: Univariate Data Visualization ������������������������������������������������������������������ 1
1.1 Distribution ................................................................................................................. 2
Visualizing the Observed Distribution .................................................................................... 2
Stacked Dot Plots and Histograms ......................................................................................... 2
Density Plots .......................................................................................................................... 6
Comparing the Observed Distribution with Expected Distributions ....................................... 9
Q-Q Plots ................................................................................................................................ 9
Density Plots ........................................................................................................................ 14
Fitting More Distributions .................................................................................................... 15
1.2 Anomalous Values .................................................................................................... 21
1.3 Summary .................................................................................................................. 30
Chapter 2: Multivariate Data Visualization ������������������������������������������������������������� 33
2.1 Distribution ............................................................................................................... 34
2.2 Anomalous Values .................................................................................................... 40
2.3 Relations Between Variables .................................................................................... 44
Assessing Homogeneity of Variance .................................................................................... 53
2.4 Summary .................................................................................................................. 59
About the Authors ���������������������������������������������������������������������������������������������������� ix
About the Technical Reviewer ��������������������������������������������������������������������������������� xi
Acknowledgments ������������������������������������������������������������������������������������������������� xiii
Introduction �������������������������������������������������������������������������������������������������������������xv

iv
Chapter 3: GLM 1 ���������������������������������������������������������������������������������������������������� 61
3.1 Conceptual Background ........................................................................................... 62
3.2 Categorical Predictors and Dummy Coding .............................................................. 65
Two-Level Categorical Predictors ........................................................................................ 65
Three- or More Level Categorical Predictors ....................................................................... 66
3.3 Interactions and Moderated Effects ......................................................................... 68
3.4 Formula Interface ..................................................................................................... 70
3.5 Analysis of Variance ................................................................................................. 72
Conceptual Background ....................................................................................................... 72
ANOVA in R ........................................................................................................................... 76
3.6 Linear Regression .................................................................................................... 79
Conceptual Background ....................................................................................................... 80
Linear Regression in R ......................................................................................................... 82
High-Performance Linear Regression .................................................................................. 99
3.7 Controlling for Confounds ....................................................................................... 102
3.8 Case Study: Multiple Linear Regression with Interactions ..................................... 113
3.9 Summary ................................................................................................................ 121
Chapter 4: GLM 2 �������������������������������������������������������������������������������������������������� 123
4.1 Conceptual Background ......................................................................................... 124
Logistic Regression ............................................................................................................ 124
Count Regression ............................................................................................................... 126
4.2 R Examples ............................................................................................................. 128
Binary Logistic Regression ................................................................................................ 129
Ordered Logistic Regression .............................................................................................. 136
Multinomial Logistic Regression ........................................................................................ 140
Poisson and Negative Binomial Regression ....................................................................... 145
4.3 Case Study: Multinomial Logistic Regression ........................................................ 153
4.4 Summary ................................................................................................................ 162
Table of ConTenTs
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