Atnaujinkite slapukų nuostatas

El. knyga: Quantitative Methods in Archaeology Using R

(Texas A & M University)

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

For archaeologists and anthropologists using quantitative methods on their data, this is the first hands-on guide to using the R statistical computing system. Basic descriptive and inferential statistics are covered as well as multivariate methods including cluster analysis, discriminant analysis, and correspondence analysis.

Quantitative Methods in Archaeology Using R is the first hands-on guide to using the R statistical computing system written specifically for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with applications to real archaeological data showing how to compute descriptive statistics, create tables, and produce a wide variety of charts and graphs. Part II addresses the major multivariate approaches used by archaeologists, including multiple regression (and the generalized linear model); multiple analysis of variance and discriminant analysis; principal components analysis; correspondence analysis; distances and scaling; and cluster analysis. Part III covers specialized topics in archaeology, including intra-site spatial analysis, seriation, and assemblage diversity.

Daugiau informacijos

The first step-by-step guide to the quantitative analysis of archaeological data using the R statistical computing system.
List of Figures
xi
List of Tables
xv
List of Boxes
xvii
Acknowledgments xix
1 Introduction
1(8)
1.1 Organization of the Book
6(3)
PART I R AND BASIC STATISTICS
9(208)
2 Introduction to R
11(25)
2.1 First Steps Using R
11(11)
2.2 Next Steps Using R
22(4)
2.3 Getting Your Data Into R
26(2)
2.4 Starting and Stopping R
28(1)
2.5 R Functions
28(2)
2.6 Getting Help
30(1)
2.7 Other Ways to Use R
31(2)
2.8 Archaeological Data for Learning R
33(3)
3 Looking at Data - Numerical Summaries
36(29)
3.1 Arithmetic with R
38(4)
3.2 Four Common Distributions
42(7)
3.3 Descriptive Statistics - Numeric
49(2)
3.4 Descriptive Statistics Using R
51(14)
4 Looking at Data - Tables
65(20)
4.1 Factors in R
65(3)
4.2 Producing Simple Tables in R
68(4)
4.3 More Than Two Variables
72(5)
4.4 Binning Numeric Variables
77(1)
4.5 Saving and Exporting Tables
78(7)
5 Looking at Data - Graphs
85(41)
5.1 True and False in R
86(2)
5.2 Plotting One or Two Categorical Variables
88(7)
5.3 One Numerical Variable
95(4)
5.4 One Numerical Variable and One Categorical Variable
99(4)
5.5 Two Numerical Variables
103(6)
5.6 More Than Two Numerical Variables
109(7)
5.7 Printing Graphs
116(10)
6 Transformations
126(18)
6.1 The Apply Family of Functions in R
127(2)
6.2 Transforming Variables (Columns)
129(7)
6.3 Transforming Observations (Rows)
136(8)
7 Missing Values
144(15)
7.1 Missing Values and Other Special Values in R
145(2)
7.2 Eliminating Cases or Variables with Missing Values
147(3)
7.3 Imputing Missing Values
150(9)
8 Confidence Intervals and Hypothesis Testing
159(31)
8.1 Programming R - Writing Functions
160(2)
8.2 Confidence Intervals
162(7)
8.3 Hypothesis Testing
169(2)
8.4 Comparing Two Samples
171(7)
8.5 Comparing More Than Two Samples
178(12)
9 Relating Variables
190(27)
9.1 Categorical Data
190(8)
9.2 Numeric Data - Association
198(6)
9.3 Numeric Data - Regression
204(13)
PART II MULTIVARIATE METHODS
217(130)
10 Multiple Regression and Generalized Linear Models
219(25)
10.1 Multiple Regression
219(13)
10.2 Regression with Dummy Variables
232(3)
10.3 Generalized Linear Models - Logistic Regression
235(9)
11 MANOVA and Discriminant Analysis
244(21)
11.1 Hotelling's T and MANOVA
245(4)
11.2 Descriptive (Canonical) Discriminant Analysis
249(6)
11.3 Predictive Discriminant Analysis
255(10)
12 Principal Components Analysis
265(14)
13 Correspondence Analysis
279(17)
14 Distances and Scaling
296(22)
14.1 Distance, Dissimilarity, and Similarity
297(6)
14.2 Multidimensional Scaling
303(8)
14.3 Comparing Distance Matrices - Mantel Tests
311(7)
15 Cluster Analysis
318(29)
15.1 K-Means Partitioning
321(13)
15.2 Hierarchical Clustering
334(8)
15.3 Other Methods
342(5)
PART III ARCHAEOLOGICAL APPROACHES TO DATA
347(68)
16 Spatial Analysis
349(30)
16.1 Circular or Directional Statistics
349(9)
16.2 Mapping Quadrat-Based Data
358(9)
16.3 Mapping Piece Plot Data
367(5)
16.4 Simple Spatial Statistics
372(7)
17 Seriation
379(18)
17.1 Distance Matrix Ordering
381(3)
17.2 Ordering the Data Matrix Directly
384(4)
17.3 Detrended Correspondence Analysis
388(2)
17.4 Principal Curves
390(7)
18 Assemblage Diversity
397(15)
18.1 Diversity, Ubiquity, and Evenness
399(4)
18.2 Sample Size and Richness
403(5)
18.3 Rarefaction Curves
408(4)
19 Conclusions
412(3)
References 415(8)
Index 423
David L. Carlson is a Professor of Anthropology at Texas A & M University, where he has been teaching quantitative methods and the R statistical system to anthropology graduate students for eight years. His research focuses on the application of quantitative methods to discover and understand patterning in the distribution of artifacts on archaeological sites. He is a co-author of Clovis Lithic Technology (2011).