1104 search results for "latex"

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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Knitr’s best hidden gem: spin

March 23, 2015
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Stop knitting & start spinning - spin can help you write reports much faster and avoid repeating yourself - Anyone who loves the idea of dynamic report generation with R is probably a big fan of knitr and its flagship function - knit. But not many people seem to know about knit's awesome cousin -...

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Regression Models, It’s Not Only About Interpretation

March 22, 2015
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Regression Models, It’s Not Only About Interpretation

Yesterday, I did upload a post where I tried to show that “standard” regression models where not performing bad. At least if you include splines (multivariate splines) to take into accound joint effects, and nonlinearities. So far, I do not discuss the possible high number of features (but with boostrap procedures, it is possible to assess something related to...

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Dark themes for writing

March 17, 2015
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Dark themes for writing

I spend much of my day sitting in front of a screen, coding or writing. To limit the strain on my eyes, I use a dark theme as much as possible. That is, I write with light colored text on a dark background. I don’t know why this is not the default in more software

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Some thoughts on Vim

March 17, 2015
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Some thoughts on Vim

by Gary R. Moser Director of Institutional Research and Planning The California Maritime Academy I recently contacted Joseph Rickert about inviting Vim guru Drew Niel (web: vimcasts.org, book: "Practical Vim: Edit Text at the Speed of Thought") to speak at the Bay Area R User Group group. Due to Drew's living in Great Britain that might not be easily...

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Helping Your Organization Migrate to R

March 16, 2015
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Helping Your Organization Migrate to R

by Bob Muenchen As the R programming environment has grown in capability and popularity, so have the number of organizations planning to migrate to it from proprietary tools. I’ve helped members of various organizations transition from SAS, SPSS and/or Stata to R (see Workshop … Continue reading →

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Simple template for scientific manuscripts in R markdown

March 12, 2015
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The good reasons to write scientific reports and manuscripts in LaTeX or Markdown are: improved document integrity (always), simplicity (not always) and reproducibility (always). I prefer the lightweight Markdown over rich but more complex LaTeX -- I think that lightweight is good for reproducibility. I am also in love with … Continue reading →

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Matrix factorization

March 10, 2015
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Matrix factorization

Or fancy words that mean very simple things. At the heart of most data mining, we are trying to represent complex things in a simple way. The simpler you can explain the phenomenon, the better you understand. It’s a little zen – compression is the same as understanding. Warning: Some math ahead.. but stick with it, it’s worth

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Econometrics Sim – 1: Endogeneity

March 9, 2015
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Econometrics Sim – 1: Endogeneity

Introduction This is the first post in a series devoted to explaining basic econometric concepts using R simulations. The topic in this post is endogeneity, which can severely bias regression estimates. I will specifically simulate endogeneity caused by an omitted variable. In future posts in this series, I’ll simulate other specification issues such as heteroskedasticity, multicollinearity, and collider … Continue reading...

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Some More Results on the Theory of Statistical Learning

March 8, 2015
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Some More Results on the Theory of Statistical Learning

Yesterday, I did mention a popular graph discussed when studying theoretical foundations of statistical learning. But there is usually another one, which is the following, Let us get back to the underlying formulas. On the traning sample, we have some empirical risk, defined as for some loss function . Why is it complicated ? From the law of large...

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