An R "meta" book
来源:互联网 发布:js自定义属性 编辑:程序博客网 时间:2024/06/06 09:29
by Joseph Rickert
I am a book person. I collect books on all sorts of subjects that interest me and consequently I have a fairly extensive collection ofR books, many of which I find to be of great value. Nevertheless, when I am asked to recommend an R book to someone new to R I am usually flummoxed.R is growing at a fantastic rate, and people coming to R for the first time span I wide range of sophistication. And besides, owning a book is kind of personal. It is one thing to go out and buy a technical book because it is required for a course, but quite an other to make a commitment to a book all on your own. Not only must it have the right content, at the right level for you, and be written in a way that you will actually read it, a book must feel right, be typeset to appeal to your sense of aesthetics, have diagrams and illustrations to draw you in, and contain enough white space to seem approachable. Furthermore, there is a burden to owning a book. There is nothing worse than making a poor selection and having a totally incomprehensible text stare at you from a shelf. Moreover, even an old friend can impose obligations. I have read deeply fromThe Elements of Statistical Learning, but not everything, so there it sits: admonishing me.
Recently, however, while crawling around CRAN, it occurred to me that there is a tremendous amount of high quality material on a wide range of topics in theContributed Documentation page that would make a perfect introduction to all sorts of people coming to R. Maybe, all it needs is a little marketing and reorganization. So, from among this treasure cache (and a few other online sources), I have assembled an R “meta” book in the following table that might be called:An R Based Introduction to Probability and Statistics with Applications.
Content
Document
Author
1
Basic Probability and Statistics
Introduction to Probability and Statistics Using R
G. Jay Kerns
2
Fitting Probability Distributions
Fitting Distributions with R
Vito Ricci
3
- Regression
- Inference
- Diagnostics
- Stepwise Regression
- Ridge Regression
- ANOVA
Practical Regression and Anova using R
Julian J. Faraway
4
Experimental Design
An R companion to Experimental Design
Vikneswaran
5
Survival Analysis
Cox Proportional-Hazards Regression for Survival Data
John Fox
6
Generalized Linear Models
Analysis of epidemiological data using R and Epicalc
Virasakdi Chongsuvivatwong
7
- Bootstrap
- Hierarchical Models
- Nonlinear Mixed Effects
icebreakeR
Andrew Robinson
8
Time Series
Time Series Analysis with R
McLeod, Yu and Mahdi
9
- Bayesian Statistics
- Gibbs Sampler
Statistics Using R with Biological Examples
Kim Seefeld and Ernst Linder
10
Machine Learning
- Decision Trees and Random Forest
- Clustering
- Outlier Detection
- Time Series Analysis and Mining
- Text Mining
- Social Network Analysis
R and Data Mining: Examples and Case Studies
Yanchang Zhao
11
Bioinformatics
- Cluster Analysis
- Classification Methods
- Markov Models
- Micro Array Analysis
Applied Statistics for Bioinformatics using R
Wim P. Krijnen
12
Forecasting
Forecasting: principles and practice
Hyndman and Athanasopoulos
13
Structural Equation Models
Structural Equation Models
John Fox
14
Credit Scoring
Guide to Credit Scoring in R
Dhruv Sharma
The content column lists the topics that I think ought to be included in a good introductory probability and statistics textbook. With a little searching, you will be able to find a discussion of each topic in the document listed to its right. Obviously, there is a lot overlap among the documents listed, since most of them are substantial works that cover much more than the few topics that I have listed.
Finally, I don’t mean to imply that the documents in my table are the best assembled in theContributed Documentation page. The table just represents my idiosyncratic way of organizing some of the material in a way that I hope newcomers will find useful. I think that collectively the contributed documents have everything one might look for in a first date with R. They are available, approachable, contain superb content written by R experts, and are replete with examples and R code. And, with a little effort, a casual first encounter could lead to long term relationship.
- An R "meta" book
- an excellent book
- #R#《The R Book》笔记
- An excellent book for design patterns beginners
- An introduction to Objective-C Meta Class
- An R Time Series Tutorial
- An R Time Series Tutorial
- YAML: Define an R function
- Design data structures for an online book reader system
- [DEEP LEARNING An MIT Press book in preparation]Linear algebra
- book
- book
- book
- book
- book
- Book
- book
- Book
- 大陆--身份证(外国人)正则表达式
- 黑马程序员——java-集合框架(二)
- UVA 12170 Easy Climb(dp+单调队列优化)
- 第一篇文章:单例模式的6种写法
- ViewGroup重写——网格容器
- An R "meta" book
- 整数因子分解算法
- Html.partial和RenderPartial的用法与区别
- 台湾--身份证(外国人)正则表达式
- 澳门--身份证(外国人)正则表达式
- 懂二进制
- 计数排序算法
- 香港--身份证(外国人)正则表达式
- hbase完全分布式安装配置