Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment
In Detail
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.
What You Will Learn
Master data management in R
Perform hypothesis tests using both parametric and nonparametric methods
Understand how to perform statistical modeling using linear methods
Model nonlinear relationships in data with kernel density methods
Use matrix operations to improve coding productivity
Utilize the observed data to model unobserved variables
Deal with missing data using multiple imputations
Simplify high-dimensional data using principal components, singular value decomposition, and factor analysis
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