随笔分类 -  R

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R语言学习分享
Include promo/activity effect into the prediction (extended ARIMA model with R)
摘要：I want to consider an approach of forecasting I really like and frequently use. It allows to include the promo campaigns (or another activities and ot... 阅读全文
posted @ 2015-04-03 16:20 payton数据之旅 阅读(314) 评论(0) 推荐(0)
Cohort Analysis and LifeCycle Grids mixed segmentation with R（转）
摘要：This is the third post aboutLifeCycle Grids. You can find the first post about the sense of LifeCycle Grids and A-Z process for creating and visualizi... 阅读全文
posted @ 2015-04-03 16:07 payton数据之旅 阅读(356) 评论(0) 推荐(0)
Customer segmentation – LifeCycle Grids, CLV and CAC with R（转）
摘要：We studied a very powerful approach for customer segmentation inthe previous post, which is based on the customer’s lifecycle. We used two metrics:fre... 阅读全文
posted @ 2015-04-03 16:04 payton数据之旅 阅读(432) 评论(0) 推荐(0)
Customer segmentation – LifeCycle Grids with R（转）
摘要：I want to share a very powerful approach for customer segmentation in this post. It is based on customer’s lifecycle, specifically onfrequencyandrecen... 阅读全文
posted @ 2015-04-03 15:59 payton数据之旅 阅读(369) 评论(0) 推荐(0)
Interactive pivot tables with R（转）
摘要：I love interactivepivot tables. That is the number one reason why I keep using spreadsheet software. The ability to look at data quickly in lots of di... 阅读全文
posted @ 2015-04-03 15:44 payton数据之旅 阅读(597) 评论(0) 推荐(0)
Targeted Learning R Packages for Causal Inference and Machine Learning（转）
摘要：Targeted learning methods build machine-learning-based estimators of parameters defined as features of the probability distribution of the data, while... 阅读全文
posted @ 2015-04-03 15:36 payton数据之旅 阅读(535) 评论(0) 推荐(0)
Sampling Distributions and Central Limit Theorem in R（转）
摘要：The Central Limit Theorem (CLT), and the concept of the sampling distribution, are critical for understanding why statistical inference works. There a... 阅读全文
posted @ 2015-03-31 11:27 payton数据之旅 阅读(417) 评论(0) 推荐(0)
Introducing: Machine Learning in R（转）
摘要：Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept lear... 阅读全文
posted @ 2015-03-27 09:43 payton数据之旅 阅读(592) 评论(0) 推荐(0)
A function to help graphical model checks of lm and ANOVA（转）
摘要：As always a more colourful version of this post is available onrpubs.Even if LM are very simple models at the basis of many more complex ones, LM stil... 阅读全文
posted @ 2015-03-26 14:34 payton数据之旅 阅读(261) 评论(0) 推荐(0)
Hash Table Performance in R: Part I（转）
摘要：What Is It?A hash table, or associative array, is awell known key-value data structure. In R there is no equivalent, but you do have some options. You... 阅读全文
posted @ 2015-03-25 09:38 payton数据之旅 阅读(242) 评论(0) 推荐(0)
reshape: from long to wide format（转）
摘要：This is to continue on the topic of using themelt/castfunctions inreshapeto convert between long and wide format of data frame. Here is the example I ... 阅读全文
posted @ 2015-02-28 10:00 payton数据之旅 阅读(569) 评论(0) 推荐(0)
BAYESIAN STATISTICS AND CLINICAL TRIAL CONCLUSIONS: WHY THE OPTIMSE STUDY SHOULD BE CONSIDERED POSITIVE（转）
摘要：Statistical approaches to randomised controlled trial analysisThe statistical approach used in the design and analysis of the vast majority of clinica... 阅读全文
posted @ 2015-02-27 09:47 payton数据之旅 阅读(330) 评论(0) 推荐(0)
How-to go parallel in R – basics + tips（转）
摘要：Today is a good day to start parallelizing your code. I’ve been using the parallel package since its integration with R (v. 2.14.0) and its much easie... 阅读全文
posted @ 2015-02-27 09:40 payton数据之旅 阅读(539) 评论(0) 推荐(0)
Bash+R: howto pass parameters from bash script to R（转）
摘要：From original post @http://analyticsblog.mecglobal.it/analytics-tools/bashr/In the world of data analysis, the term automation runs hand in hand with ... 阅读全文
posted @ 2015-02-12 11:03 payton数据之旅 阅读(366) 评论(0) 推荐(0)
The dplyr package has been updated with new data manipulation commands for filters, joins and set operations.（转）
摘要：dplyr 0.4.0January 9, 2015 inUncategorizedI’m very pleased to announce that dplyr 0.4.0 is now available from CRAN. Get the latest version by running:... 阅读全文
posted @ 2015-02-11 10:13 payton数据之旅 阅读(406) 评论(0) 推荐(0)
R语言与分类算法的绩效评估（转）
摘要：关于分类算法我们之前也讨论过了KNN、决策树、naivebayes、SVM、ANN、logistic回归。关于这么多的分类算法，我们自然需要考虑谁的表现更加的优秀。 既然要对分类算法进行评价，那么我们自然得有评价依据。到目前为止，我们讨论分类的有效性都是基于分类成功率来说的，但是这个指标科学吗... 阅读全文
posted @ 2015-02-09 11:29 payton数据之旅 阅读(1533) 评论(0) 推荐(0)
More 3D Graphics (rgl) for Classification with Local Logistic Regression and Kernel Density Estimates (from The Elements of Statistical Learning)（转）
摘要：This post builds on aprevious post, but can be read and understood independently.As part of my course on statistical learning, we created 3D graphics ... 阅读全文
posted @ 2015-02-09 10:17 payton数据之旅 阅读(307) 评论(0) 推荐(0)
决策树模型比较：C4.5，CART，CHAID，QUEST
摘要：（1）C4.5算法的特点为：输入变量（自变量）：为分类型变量或连续型变量。输出变量（目标变量）：为分类型变量。连续变量处理：N等分离散化。树分枝类型：多分枝。分裂指标：信息增益比率gain ratio（分裂后的目标变量取值变异较小，纯度高）前剪枝：叶节点数是否小于某一阈值。后剪枝：使用置信度法和减少... 阅读全文
posted @ 2015-02-04 13:13 payton数据之旅 阅读(5110) 评论(0) 推荐(0)
R + ggplot2 Graph Catalog（转）
摘要：Joanna Zhao’s and Jenny Bryan’sR graph catalogis meant to be a complement to the physical book,Creating More Effective Graphs, but it’s a really nice ... 阅读全文
posted @ 2015-02-04 10:11 payton数据之旅 阅读(486) 评论(0) 推荐(0)
My "Top 5 R Functions"（转）
摘要：In preparation for a R Workgroup meeting, I started thinking about what would be my "Top 5 R Functions". I ruled out the functions for basic mechanics... 阅读全文
posted @ 2015-02-04 10:04 payton数据之旅 阅读(192) 评论(0) 推荐(0)

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