# 1186 search results for "LaTeX"

## Exploratory Data Analysis: Conceptual Foundations of Empirical Cumulative Distribution Functions

Introduction Continuing my recent series on exploratory data analysis (EDA), this post focuses on the conceptual foundations of empirical cumulative distribution functions (CDFs); in a separate post, I will show how to plot them in R.  (Previous posts in this series include descriptive statistics, box plots, kernel density estimation, and violin plots.) To give you

## FuzzyNumbers-0.3-1 released

June 23, 2013
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A new version of the FuzzyNumbers package for R has just been submitted to the CRAN archive. Check out our step-by-step tutorial. ** FuzzyNumbers Package CHANGELOG ** ********************************************************************* 0.3-1 /2013-06-23/ * piecewiseLinearApproximation() - general case (any knot.n) for method="NearestEuclidean" now…Read more ›

## Printing R help files in the console or in knitr documents

June 18, 2013
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Yesterday, I was creating a knitr document based on a script, and was looking for a way to include content from an R help file. The script, which was a teaching document, had a help() command for when the author wanted to refer readers to R documentation. I wanted that text in my final document, though. There’s no...

## Model Selection in Bayesian Linear Regression

June 17, 2013
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$Model Selection in Bayesian Linear Regression$

Previously I wrote about performing polynomial regression and also about calculating marginal likelihoods. The data in the former and the calculations of the latter will be used here to exemplify model selection. Consider data generated by and suppose we wish to fit a polynomial of degree 3 to the data. There are then 4 regression The post Model...

## The equivalence of the ellipsis argument and an infinite set of closures

June 14, 2013
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$The equivalence of the ellipsis argument and an infinite set of closures$

This post is about a practical application of a topic I discuss in my book. In my book, I prove …Continue reading »

## Top 100 R packages for 2013 (Jan-May)!

June 13, 2013
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(This article was first published on R-statistics blog » RR-statistics blog, and kindly contributed to R-bloggers) What are the top 100 (most downloaded) R packages in 2013? Thanks to the recent release of RStudio of their “0-cloud” CRAN log files (but without including downloads from the primary CRAN mirror or any of the 88 other CRAN mirrors), we can now answer this question...

## The null model for age effects with overdispersed infection

June 12, 2013
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$\inline k$

How does overdispersion of infections affect the behavior of the multiple-infection model? I redefine the model to account for overdispersion, assuming the same overdispersion occurs in both age classes. The parameter varies inversely with the degree of overdispersion. Again, the classes are demographically identical, and infection affects mortality but not growth: \[\begin{aligned} \frac{dJ}{dt}...

## A Null Model for Age Effects in Disease with Multiple Infections

June 11, 2013
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$\inline SI$

Here’s a little thought exercise I did that has caused me to go back and restart my Sudden Oak Death modeling in a new framework. Feedback welcome. I’m especially interested in relevant literature – I haven’t found many good examples of macroparasite/multiple infection models with age structure. Introduction Cobb et al. (2012) develop two models of forest stand demography...

## Le Monde puzzle [#822]

June 10, 2013
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For once Le Monde math puzzle is much more easily solved on a piece of paper than in R, even in a plane from Roma: Given a partition of the set {1,…,N} in k groups, one considers the collection of all subsets of  the set {1,…,N} containing at least one element from each group. Show

## Exploratory Data Analysis: Kernel Density Estimation in R on Ozone Pollution Data in New York and Ozonopolis

Introduction Recently, I began a series on exploratory data analysis; so far, I have written about computing descriptive statistics and creating box plots in R for a univariate data set with missing values.  Today, I will continue this series by analyzing the same data set with kernel density estimation, a useful non-parametric technique for visualizing