# 1267 search results for "LaTeX"

## BCEs0

July 20, 2013
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BCEs0 is the new R package I've written \$-\$ well, nearly finished to, anyway; it should be ready in version 1.0 in the next few days. The acronym stands for Bayesian models for Cost-Effectiveness with structural 0s, and it basically implements the mode...

## Optimising a Noisy Objective Function

July 16, 2013
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$Optimising a Noisy Objective Function$

I am busy with a project where I need to calibrate the Heston Model to some Asian options data. The model has been implemented as a function which executes a Monte Carlo (MC) simulation. As a result, the objective function is rather noisy. There are a number of algorithms for dealing with this sort of problem, and

## Getting Started with Reproducible Research: A chapter from my new book

July 15, 2013
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(This article was first published on Christopher Gandrud (간드루드 크리스토파), and kindly contributed to R-bloggers) This is an abridged excerpt from Chapter 2 of my new book Reproducible Research with R and RStudio. It’s published by Chapman & Hall/CRC Press. You can purchase it on Amazon. “Search inside this book” includes a complete table of contents. Researchers often start...

## Exploratory Data Analysis: Conceptual Foundations of Histograms – Illustrated with New York’s Ozone Pollution Data

Introduction Continuing my recent series on exploratory data analysis (EDA), today’s post focuses on histograms, which are very useful plots for visualizing the distribution of a data set.  I will discuss how histograms are constructed and use histograms to assess the distribution of the “Ozone” data from the built-in “airquality” data set in R.  In

## Veterinary Epidemiologic Research: Modelling Survival Data – Parametric and Frailty Models

July 5, 2013
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$Veterinary Epidemiologic Research: Modelling Survival Data – Parametric and Frailty Models$

Last post on modelling survival data from Veterinary Epidemiologic Research: parametric analyses. The Cox proportional hazards model described in the last post make no assumption about the shape of the baseline hazard, which is an advantage if you have no idea about what that shape might be. With a parametric survival model, the survival time

## A Brief Look at Mixture Discriminant Analysis

July 2, 2013
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Lately, I have been working with finite mixture models for my postdoctoral work on data-driven automated gating. Given that I had barely scratched the surface with mixture models in the classroom, I am becoming increasingly comfortable with them. With this in mind, I wanted to explore their application to classification because there are times when a single class is clearly made up of...

## Some Common Approaches for Analyzing Likert Scales and Other Categorical Data

July 1, 2013
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$Some Common Approaches for Analyzing Likert Scales and Other Categorical Data$

Analyzing Likert scale responses really comes down to what you want to accomplish (e.g. Are you trying to provide a formal report with probabilities or are you trying to simply understand the data better). Sometimes a couple of graphs are sufficient and a formalize statistical test isn’t even necessary. However, with how easy it is

## Learning R: Parameter Fitting for Models Involving Differential Equations

June 30, 2013
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$Learning R: Parameter Fitting for Models Involving Differential Equations$

It looks like MATLAB, Octave and Python seem to be the preferred tools for scientific and engineering analysis (especially those involving physical models with differential equations). However as part of my learning R experience, I wanted to check out some … Continue reading →

## R snippets for vim-SnipMate

June 26, 2013
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Vim is my editor of choice, reasonable so, whether it be for coding C++, LaTeX or even R. I’ve used RStudio, which even has a Vim-Mode, but I still prefer to use Vim. Vim has it’s own R plugin, namely Vim-R-plugin, but this post is about snippets. SnipMate is an awesome auto-completion plugin for Vim The post R...

## Exploratory Data Analysis: 2 Ways of Plotting Empirical Cumulative Distribution Functions in R

$Exploratory Data Analysis: 2 Ways of Plotting Empirical Cumulative Distribution Functions in R$

Introduction Continuing my recent series on exploratory data analysis (EDA), and following up on the last post on the conceptual foundations of empirical cumulative distribution functions (CDFs), this post shows how to plot them in R.  (Previous posts in this series on EDA include descriptive statistics, box plots, kernel density estimation, and violin plots.) I