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Titre : A Beginner’s Guide to R Type de document : Guide/Manuel Auteurs : Alain F. Zuur, Auteur ; Elena N. Ieno, Auteur ; Erik H. W. G. Meesters, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2009 Collection : Use R! Importance : 218 p. ISBN/ISSN/EAN : 978-0-387-93837-0 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] fonction mathématique
[Termes IGN] outil informatique
[Termes IGN] R (langage)
[Termes IGN] variableRésumé : (Auteur) [Introduction] Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R. Note de contenu : - Introduction
- Getting Data into R
- Accessing Variables and Managing Subsets of Data
- Simple Functions
- An Introduction to Basic Plotting Tools
- Loops and Functions
- Graphing Tools
- An Introduction to the Lattice Package
- Common R MistakesNuméro de notice : 26291 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Manuel informatique DOI : 10.1007/978-0-387-93837-0 En ligne : https://doi.org/10.1007/978-0-387-93837-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94960
Titre : Applied spatial data analysis with R Type de document : Monographie Auteurs : R.S. Bivand, Auteur ; Edzer J. Pebesma, Auteur ; V. Gómez-Rubio, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2008 Collection : Use R! Importance : 374 p. Format : 15 x 23 cm ISBN/ISSN/EAN : 978-0-387-78170-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de données
[Termes IGN] données localisées
[Termes IGN] géostatistique
[Termes IGN] R (langage)
[Termes IGN] système d'information géographiqueRésumé : (Editeur) This book is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. Note de contenu : 1. Hello World: Introducing Spatial Data
1.1 Applied Spatial Data Analysis
1.2 Why Do We Use R
1.3 R and GIS
1.4 Types of Spatial Data
1.5 Storage and Display
1.6 Applied Spatial Data Analysis
1.7 R Spatial Resources
Part 1 - Handling Spatial Data in R
2. Classes for Spatial Data in R
2.1 Introduction
2.2 Classes and Methods in R
2.3 Spatial Objects
2.4 SpatialPoints
2.5 SpatialLines
2.6 SpatialPolygons
2.7 SpatialGrid and SpatialPixel Objects
3. Visualising Spatial Data
3.1 The Traditional Plot System
3.2 Trellis/Lattice Plots with spplot
3.3 Interacting with Plots
3.4 Colour Palettes and Class Intervals
4. Spatial Data Import and Export
4.1 Coordinate Reference Systems
4.2 Vector File Formats
4.3 Raster File Formats
4.4 Grass
4.5 Other Import/Export Interfaces
4.6 Installing rgdal
5. Further Methods for Handling Spatial Data
5.1 Support
5.2 Overlay
5.3 Spatial Sampling
5.4 Checking Topologies
5.5 Combining Spatial Data
5.6 Auxiliary Functions
6. Customising Spatial Data Classes and Methods
6.1 Programming with Classes and Methods
6.2 Animal Track Data in Package Trip
6.3 Multi-Point Data: SpatialMultiPoints
6.4 Hexagonal Grids
6.5 Spatio-Temporal Grids
6.6 Analysing Spatial Monte Carlo Simulations
6.7 Processing Massive Grids
Part 2 - Analysing Spatial Data
7. Spatial Point Pattern Analysis
7.1 Introduction
7.2 Packages for the Analysis of Spatial Point Patterns
7.3 Preliminary Analysis of a Point Pattern
7.4 Statistical Analysis of Spatial Point Processes
7.5 Some Applications in Spatial Epidemiology
7.6 Further Methods for the Analysis of Point Patterns
8. Interpolation and Geostatistics
8.1 Introduction
8.2 Exploratory Data Analysis
8.3 Non-Geostatistical Interpolation Methods
8.4 Estimating Spatial Correlation: The Variogram
8.5 Spatial Prediction
8.6 Model Diagnostics
8.7 Geostatistical Simulation
8.8 Model-Based Geostatistics and Bayesian Approaches
8.9 Monitoring Network Optimization
8.10 Other R Packages for Interpolation and Geostatistics
9. Areal Data and Spatial Autocorrelation
9.1 Introduction
9.2 Spatial Neighbours
9.3 Spatial Weights
9.4 Spatial Autocorrelation: Tests
10. Modelling Areal Data
10.1 Introduction
10.2 Spatial Statistics Approaches
10.3 Mixed-Effects Models
10.4 Spatial Econometrics Approaches
10.5 Other Methods
11. Disease Mapping
11.1 Introduction
11.2 Statistical Models
11.3 Spatially Structured Statistical Models
11.4 Bayesian Hierarchical Models
11.5 Detection of Clusters of Disease
11.6 Other Topics in Disease MappingNuméro de notice : 20853 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Monographie Accessibilité hors numérique : Non accessible via le SUDOC Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=63150