46 search results for "rattle"

Review: Data Mining with Rattle and R

October 7, 2015
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Review: Data Mining with Rattle and R

I read Data Mining with Rattle and R by Graham Williams over a year ago. It's not a new book and I've just been tardy in writing up a review. That's not to say that I have not used the book in the interim: it's been on my desk at work ever since and I've The post

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Video: R, RStudio, Rcmdr & rattle

September 7, 2012
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I did a screencast for my co-workers to show how to get started with R, specifically what a base installation of R looks like, then showing how to improve your workflow using RStudio, Rcmdr or rattle.  The examples are somewhat … Continue reading →Video: R, RStudio, Rcmdr & rattle is an article from randyzwitch.com, a...

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Rattle: an R GUI

December 16, 2007
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Rattle: an R GUI

Rattle is a graphical interface to R. It supports basic data management tasks, as well as a number of different modeling functions. I haven’t had a chance to test it out yet since I don’t have my R installation  completely up to date, but it looks promising. For all of R’s strengths, one weakness is

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Rated R: Recommended Reading

November 19, 2015
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Rated R: Recommended Reading

by Joseph Rickert What are you reading? - and what are you recommending to friends, colleagues, and students who want to learn something about R programming? A quick search of Amazon will show that there are several new R books proposed for 2016; but of course, new doesn't necessarily mean better. I fully expect that many new books in...

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Data Mining with R: Generate knowledge from data with the eoda R-Academy

October 21, 2015
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The course “Data Mining with R”, which takes place from 9th to 10th November 2015 in Kassel, Germany, teaches the most important methods to expose correlations in data and inherent patterns. The wide set of methods can be used for almost every application area. From customer segmentation to timely recognition of machines, users gain knowledge

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Goals for the New R Consortium

July 28, 2015
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Goals for the New R Consortium

by Bob Muenchen The recently-created R Consortium consists of companies that are deeply involved in R such as RStudio, Microsoft/Revolution Analytics, Tibco, and others. The Consortium’s goals include advancing R’s worldwide promotion and support, encouraging user adoption, and improving documentation … Continue reading →

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Part 4a: Modelling – predicting the amount of rain

April 6, 2015
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Part 4a: Modelling – predicting the amount of rain

In the fourth and last part of this series, we will build several predictive models and evaluate their accuracies. In Part 4a, our dependent value will be continuous, and we will be predicting the daily amount of rain. Then, in Part 4b, we will deal with the case of a binary outcome, which means we will assign probabilities to...

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Spliting a Node in a Tree

March 23, 2015
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Spliting a Node in a Tree

If we grow a tree with standard functions in R, on the same dataset used to introduce classification tree in some previous post, > MYOCARDE=read.table( + "http://freakonometrics.free.fr/saporta.csv", + head=TRUE,sep=";") > library(rpart) > cart<-rpart(PRONO~.,data=MYOCARDE) we get > library(rpart.plot) > library(rattle) > prp(cart,type=2,extra=1) The first step is to split the first node (based on the whole dataset). To split it, we...

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Getting Data From An Online Source

March 6, 2015
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Getting Data From One Online SourceRobert NorbergHello world. It’s been a long time since I posted anything here on my blog. I’ve been busy getting my Masters degree in statistical computing and I haven’t had much free time to blog. But I’ve writing R code as much as ever. Now, with graduation approaching, I’m job hunting and I thought it would...

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Extracting Latent Variables from Rating Scales: Factor Analysis vs. Nonnegative Matrix Factorization

August 21, 2014
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Extracting Latent Variables from Rating Scales: Factor Analysis vs. Nonnegative Matrix Factorization

For many of us, factor analysis provides a gateway to learning how to run and interpret nonnegative matrix factorization (NMF). This post will analyze a set of ratings on a 218 item adjective checklist using both principal axis factor analysis and NMF....

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