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Using R for Numerical Analysis in Science and Engineering için kapak resmi
Başlık:
Using R for Numerical Analysis in Science and Engineering
Yazar:
Bloomfield, Victor A., author.
ISBN:
9781315373799
Basım Bilgisi:
First edition.
Fiziksel Tanımlama:
1 online resource (359 pages) : 133 illustrations, text file, PDF.
Seri:
Chapman & Hall/CRC The R Series

Chapman & Hall/CRC The R Series.
İçerik:
Introduction --Obtaining and Installing R --Learning R --Learning Numerical Methods --Finding Help --Augmenting R with Packages --Learning More about R --Calculating -- Basic Operators and Functions --Complex Numbers --Numerical Display, Round-Off Error, and Rounding --Assigning Variables --Relational Operators --Vectors --Matrices --Time and Date Calculations --Graphing -- Scatter Plots --Function Plots --Other Common Plots --Customizing Plots --Error Bars --Superimposing Vectors in a Plot --Modifying Axes --Adding Text and Math Expressions --Placing Several Plots in a Figure --Two- and Three-Dimensional Plots --The Plotrix Package --Animation --Additional Plotting Packages --Programming and Functions -- Conditional Execution: If and If Else --Loops --User-Defined Functions --Debugging --Built-in Mathematical Functions --Special Functions of Mathematical Physics --Polynomial Functions in Packages --Case Studies --Solving Systems Of Algebraic Equations -- Finding the Zeroes of a Polynomial --Finding the Zeroes of a Function --Systems of Linear Equations: Matrix Solve --Matrix Inverse --Singular Matrix --Overdetermined Systems and Generalized Inverse --Sparse Matrices --Matrix Decomposition --Systems of Nonlinear Equations --Case Studies --Numerical Differentiation and Integration -- Numerical Differentiation --Numerical Integration --Symbolic Manipulations in R --Case Studies --Optimization -- One-Dimensional Optimization --Multi-Dimensional Optimization with Optim() --Other Optimization Packages --Optimization with Constraints --Global Optimization with Many Local Minima --Linear and Quadratic Programming --Mixed-Integer Linear Programming --Case Study --Ordinary Differential Equations --Euler Method --Improved Euler Method --deSolve Package --Matrix Exponential Solution for Sets of Linear ODEs--Events and Roots --Difference Equations --Delay Differential Equations --Differential Algebraic Equations --rootSolve for Steady State Solutions of Systems of ODEs--bvpSolve Package for Boundary Value ODE Problems --Stochastic Differential Equations: Gillespiessa Package --Case Studies --Partial Differential Equations --Diffusion Equation --Wave Equation --Laplaces Equation --Solving PDEs with the Reactran Package --Examples with the Reactran Package --Case Studies --Analyzing Data --Getting Data into R --Data Frames --Summary Statistics for a Single Data Set --Statistical Comparison of Two Samples --Chi-Squared Test for Goodness of Fit --Correlation --Principal Component Analysis --Cluster Analysis --Case Studies --Fitting Models To Data -- Fitting Data with Linear Models --Fitting Data with Nonlinear Models --Inverse Modeling of ODEs with the FME Package --Improving the Convergence of Series: Pad and Shanks --Interpolation --Time Series, Spectrum Analysis, and Signal Processing --Case Studies
Özet:
Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R’s powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also: Explains how to statistically analyze and fit data to linear and nonlinear modelsExplores numerical differentiation, integration, and optimizationDescribes how to find eigenvalues and eigenfunctionsDiscusses interpolation and curve fittingConsiders the analysis of time series Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.
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