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Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization by Matt Wiley, Joshua F. Wiley

• Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization
• Matt Wiley, Joshua F. Wiley
• Page: 638
• Format: pdf, ePub, mobi, fb2
• ISBN: 9781484228715
• Publisher: Apress

Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization

Good ebooks to download Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization (English Edition) FB2 by Matt Wiley, Joshua F. Wiley

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You’ll Learn Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).

The Immune Landscape of Cancer - ScienceDirect
Introduction. The Cancer Genome Atlas (TCGA) has profoundly illuminated the genomic landscape of human malignancy. Genomic and transcriptomic data derived from bulk tumor samples have been used to study the tumor microenvironment (TME), and measures of immune infiltration define molecular subtypes of ovarian, melanoma, and pancreatic cancer (Bailey et al., 2016, The Cancer Genome Atlas Network Statistical Methods for Machine Learning
Welcome to the book: “Statistical Methods for Machine Learning“. I designed this book to teach machine learning practitioners, like you, step-by-step the basics of statistical methods with concrete and executable examples in Python. Introduction to Time Series Forecasting With Python
Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut Collection and Processing of Data from Wrist Wearable ...
2.1. Devices. Nowadays, there are many different vendors of wearable devices. The left side of Figure 1 shows the market share of the main devices based on data from the whole 2015 year [].Fitbit was the leading company in the sector of smartbands as it covered more than one third of the market share. Titles: Safari for Schools - Safari Books Online
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