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R Companion for Sampling: Design and Analysis, Third Edition [Mīkstie vāki]

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  • Formāts: Paperback / softback, 222 pages, height x width: 254x178 mm, weight: 500 g, 24 Line drawings, black and white; 24 Illustrations, black and white
  • Izdošanas datums: 25-Nov-2021
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 1032135948
  • ISBN-13: 9781032135946
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R Companion for Sampling: Design and Analysis, Third EditionPalielināt
  • Formāts: Paperback / softback, 222 pages, height x width: 254x178 mm, weight: 500 g, 24 Line drawings, black and white; 24 Illustrations, black and white
  • Izdošanas datums: 25-Nov-2021
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 1032135948
  • ISBN-13: 9781032135946
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781032135946.html
Keywords:
The R Companion for Sampling: Design and Analysis, designed to be read alongside Sampling: Design and Analysis, Third Edition by Sharon L. Lohr (SDA; 2022, CRC Press), shows how to use functions in base R and contributed packages to perform calculations for the examples in SDA.

No prior experience with R is needed. Chapter 1 tells you how to obtain R and RStudio, introduces basic features of the R statistical software environment, and helps you get started with analyzing data.

Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA, with code, output, and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors.

R features and functions are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book, you will know how to use R to select and analyze almost any type of probability sample.

All R code and data sets used in this book are available online to help you develop your skills analyzing survey data from social and public opinion research, public health, crime, education, business, agriculture, and ecology.
Preface xi
1 Getting Started
1(14)
1.1 Obtaining the Software
2(1)
1.2 Installing R Packages
2(2)
1.3 R Basics
4(1)
1.4 Reading Data into R
5(2)
1.5 Saving Output
7(3)
1.6 Integrating R Output into LATEX Documents
10(2)
1.7 Missing Data
12(1)
1.8 Summary, Tips, and Warnings
13(2)
2 Simple Random Sampling
15(12)
2.1 Selecting a Simple Random Sample
15(3)
2.2 Computing Statistics from a Simple Random Sample
18(6)
2.3 Additional Code for Exercises
24(1)
2.4 Summary, Tips, and Warnings
25(2)
3 Stratified Sampling
27(14)
3.1 Allocation Methods
27(3)
3.2 Selecting a Stratified Random Sample
30(2)
3.3 Computing Statistics from a Stratified Random Sample
32(4)
3.4 Estimating Proportions from a Stratified Random Sample
36(1)
3.5 Additional Code for Exercises
37(1)
3.6 Summary, Tips, and Warnings
38(3)
4 Ratio and Regression Estimation
41(16)
4.1 Ratio Estimation
41(3)
4.2 Regression Estimation
44(2)
4.3 Domain Estimation
46(2)
4.4 Poststratification
48(1)
4.5 Ratio Estimation with Stratified Sampling
49(1)
4.6 Model-Based Ratio and Regression Estimation
50(4)
4.7 Summary, Tips, and Warnings
54(3)
5 Cluster Sampling with Equal Probabilities
57(12)
5.1 Estimates from One-Stage Cluster Samples
57(2)
5.2 Estimates from Multi-Stage Cluster Samples
59(4)
5.3 Model-Based Design and Analysis for Cluster Samples
63(2)
5.4 Additional Code for Exercises
65(2)
5.5 Summary, Tips, and Warnings
67(2)
6 Sampling with Unequal Probabilities
69(16)
6.1 Selecting a Sample with Unequal Probabilities
69(2)
6.1.1 Sampling with Replacement
69(1)
6.1.2 Sampling without Replacement
70(1)
6.2 Selecting a Two-Stage Cluster Sample
71(6)
6.3 Computing Estimates from an Unequal-Probability Sample
77(6)
6.3.1 Estimates from with-Replacement Samples
77(2)
6.3.2 Estimates from without-Replacement Samples
79(4)
6.4 Summary, Tips, and Warnings
83(2)
7 Complex Surveys
85(22)
7.1 Selecting a Stratified Two-Stage Sample
85(3)
7.2 Estimating Quantiles
88(1)
7.3 Computing Estimates from Stratified Multistage Samples
89(3)
7.4 Univariate Plots from Complex Surveys
92(3)
7.5 Scatterplots from Complex Surveys
95(8)
7.6 Additional Code for Exercises
103(2)
7.7 Summary, Tips, and Warnings
105(2)
8 Nonresponse
107(6)
8.1 How R Functions Treat Missing Data
107(1)
8.2 Poststratification and Raking
108(2)
8.3 Imputation
110(2)
8.4 Summary, Tips, and Warnings
112(1)
9 Variance Estimation in Complex Surveys
113(16)
9.1 Replicate Samples and Random Groups
113(3)
9.2 Constructing Replicate Weights
116(10)
9.2.1 Balanced Repeated Replication
117(3)
9.2.2 Jackknife
120(2)
9.2.3 Bootstrap
122(2)
9.2.4 Replicate Weights and Nonresponse Adjustments
124(2)
9.3 Using Replicate Weights from a Survey Data File
126(1)
9.4 Summary, Tips, and Warnings
127(2)
10 Categorical Data Analysis in Complex Surveys
129(10)
10.1 Contingency Tables and Odds Ratios
129(2)
10.2 Chi-Square Tests
131(2)
10.3 Loglinear Models
133(4)
10.4 Summary, Tips, and Warnings
137(2)
11 Regression with Complex Survey Data
139(16)
11.1 Straight Line Regression with a Simple Random Sample
139(3)
11.2 Linear Regression for Complex Survey Data
142(3)
11.3 Using Regression to Compare Domain Means
145(4)
11.4 Logistic Regression
149(2)
11.5 Additional Resources and Code
151(1)
11.6 Summary, Tips, and Warnings
152(3)
12 Additional Topics for Survey Data Analysis
155(8)
12.1 Two-Phase Sampling
155(2)
12.2 Estimating the Size of a Population
157(4)
12.2.1 Ratio Estimation of Population Size
157(2)
12.2.2 Loglinear Models with Multiple Lists
159(2)
12.3 Small Area Estimation
161(1)
12.4 Summary
162(1)
A Data Set Descriptions 163(34)
Bibliography 197(8)
Index 205
Yan Lu is Associate Professor of Statistics at the University of New Mexico. Her research interests include survey sampling, mixed models, nonparametric regression, and data mining. Recent publications develop new statistical methods for combining data from multiple surveys, selecting probability samples from massive data streams, and applying nonparametric regression to survey data.

Sharon L. Lohr, the author of Measuring Crime: Behind the Statistics, has published widely about survey sampling and statistical methods for education, public policy, law, and crime. She is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute, and has received the Gertrude M. Cox, Morris Hansen, and Deming Awards. Formerly Deans Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a statistical consultant and writer.