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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
material is concerned, specically the rights of translation, reprinting, reuse of illustrations, recitation,
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neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or
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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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