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

Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization
Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization
By 作者: Matt Wiley – Joshua F. Wiley
ISBN-10 书号: 1484228715
ISBN-13 书号: 9781484228715
Edition 版本: 1st ed.
Release Finelybook 出版日期: 2019-02-21
pages 页数: (638 )

$49.99

Book Description to Finelybook sorting

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

Cover
Front Matter
1.Univariate Data Visualization
2.Multivariate Data Visualization
3.GLM1
4.GLM2
5.GAMS
6.L:Introduction
7.ML:Unsupervised
8.L:Supervised
9.Missing Data
10.GLOMs:Introduction
11.GLOMs:Linear
12.GLMs:Advanced
13.Modelling IIV
Back Matter

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