1101 search results for "latex"

Knitr with Bio7

April 16, 2015
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16.04.2015 In Bio7 it is possible to write a documentation in knitr. A simple visual and textual HTML editor (based on JavaFX) and an embedded Latex editor (based on TeXclipse) helps in the creation of a *.html or *.pdf documentation. For the *.pdf creation a TeX environment has to be available (Windows e.g. MiKTeX). Overview

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Bernouilli, Montmort and Waldegrave

April 14, 2015
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Bernouilli, Montmort and Waldegrave

In the last issue of Statistical Science, David Belhouse   and Nicolas Fillion published an accounting of a discussion between Pierre Rémond de Montmort, Nicolaus Bernoulli—”the” Bernoulli associated with the St. Petersburg paradox—, and Francis Waldegrave, about the card game of Le Her (or Hère, for wretch). Here is the abridged

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Beautiful plots while simulating loss in two-part procrustes problem

April 14, 2015
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Beautiful plots while simulating loss in two-part procrustes problem

Today I was working on a two-part procrustes problem and wanted to find out why my minimization algorithm sometimes does not converge properly or renders unexpected results. The loss function to be minimized is with denoting the Frobenius norm, is an unknown scalar and an unknown rotation matrix, i.e. . , and are four real

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Write an R Journal article with knitr

April 11, 2015
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Write an R Journal article with knitr

R Journal The R Journal publishs peer-reviewed short to medium length articles covering topics that might be of interest to users or developers of R. It is a welcome platform to spread word of new packages. As most academic journals, it has strict guidelines regarding the format of submitted articles. Traditionally, these have been provided with a The post

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Classification with Categorical Variables (the fuzzy side)

April 9, 2015
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Classification with Categorical Variables (the fuzzy side)

The Gaussian and the (log) Poisson regressions share a very interesting property, i.e. the average predicted value is the empirical mean of our sample. > mean(predict(lm(dist~speed,data=cars))) 42.98 > mean(cars$dist) 42.98 One can prove that it is also the prediction for the average individual in our sample > predict(lm(dist~speed,data=cars), + newdata=data.frame(speed=mean(cars$speed))) 42.98 The geometric interpretation is that the...

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an email exchange about integral representations

April 7, 2015
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an email exchange about integral representations

I had an interesting email exchange with a (German) reader of Introducing Monte Carlo Methods with R in the past days, as he had difficulties with the validation of the accept-reject algorithm via the integral in that it took me several iterations to realise the

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Makefiles and RMarkdown

March 28, 2015
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Makefiles and RMarkdown

Quite some time ago (October 2013, according to Amazon), I bought a copy of “Reproducible Research with R and RStudio” by Christopher Gandrud. And it was awesome. Since then, I’ve been using knitr and RMarkdown quite a lot. However, until recently, I never bothered with a makefile. At the time, I had assumed that it

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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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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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