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Mastering scientific computing with R /
"With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions. Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing,
2015.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Gerrard, Paul, |e author. | |
| 245 | 1 | 0 | |a Mastering scientific computing with R / |c Paul Gerrard, Radia M. Johnson. |
| 246 | 1 | 4 | |a Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment |
| 264 | 1 | |a Birmingham, England : |b Packt Publishing, |c 2015. | |
| 264 | 2 | |a [Sebastapol, California] : |b Safari Books Online, |c 2016. | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 500 | |a Includes index. | ||
| 520 | |a "With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions. Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method."-- |c Provided by publisher | ||
| 588 | 0 | |a Online resource; title from cover (viewed August 3, 2016). | |
| 505 | 0 | |a Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Programming with R -- Data structures in R -- Atomic vectors -- Operations on vectors -- Lists -- Attributes -- Factors -- Multidimensional arrays -- Matrices -- Data frames -- Loading data into R -- Saving data frames -- Basic plots and the ggplot2 package -- Flow control -- The for() loop -- The apply() function -- The if() statement -- The while() loop -- The repeat{} and break statement | |
| 505 | 8 | |a FunctionsGeneral programming and debugging tools -- Summary -- Chapter 2: Statistical Methods with R -- Descriptive statistics -- Data variability -- Confidence intervals -- Probability distributions -- Fitting distributions -- Higher order moments of a distribution -- Other statistical tests to fit distributions -- The propagate package -- Hypothesis testing -- Proportion tests -- Two sample hypothesis tests -- Unit root tests -- Summary -- Chapter 3: Linear Models -- An overview of statistical modeling -- Model formulas | |
| 505 | 8 | |a Explanatory variables interactionsError terms -- The intercept as parameter 1 -- Updating a model -- Linear regression -- Plotting a slope -- Analysis of variance -- Generalized linear models -- Generalized additive models -- Linear discriminant analysis -- Principal component analysis -- Clustering -- Summary -- Chapter 4: Nonlinear Methods -- Nonparametric and parametric models -- The adsorption and body measures datasets -- Theory-driven nonlinear regression -- Visually exploring nonlinear relationships -- Extending the linear framework | |
| 505 | 8 | |a Polynomial regressionPerforming a polynomial regression in R -- Spline regression -- Nonparametric nonlinear methods -- Kernel regression -- Kernel weighted local polynomial fitting -- Optimal bandwidth selection -- A practical scientific application of kernel regression -- Locally weighted polynomial regression and the loess function -- Nonparametric methods with the np package -- Nonlinear quantile regression -- Summary -- Chapter 5: Linear Algebra -- Matrices and linear algebra -- Matrices in R -- Vectors in R -- Matrix notation | |
| 505 | 8 | |a The physical functioning datasetBasic matrix operations -- Element-wise matrix operations -- Matrix subtraction -- Matrix addition -- Matrix sweep -- Basic matrix-wise operations -- Transposition -- Matrix multiplication -- Matrix inversion -- Determinants -- Triangular matrices -- Matrix decomposition -- QR decomposition -- Eigenvalue decomposition -- Lower upper decomposition -- Cholesky decomposition -- Singular value decomposition -- Applications -- Rasch analysis using linear algebra and a paired comparisons matrix | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a R (Computer program language) | |
| 650 | 0 | |a Science |x Data processing. | |
| 650 | 0 | |a Open source software. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Sciences |x Informatique. | |
| 650 | 6 | |a Logiciels libres. | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
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| 650 | 7 | |a R (Computer program language) |2 fast | |
| 650 | 7 | |a Science |x Data processing |2 fast | |
| 655 | 4 | |a Computer Science; Programming Languages; Computers. | |
| 700 | 1 | |a Johnson, Radia M., |e author. | |
| 776 | 0 | 8 | |i Print version: |n Druck-Ausgabe |t Gerrard, Paul. Mastering Scientific Computing with R |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=946186 |z Texto completo |
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