1239 search results for "latex"

Trivial, but useful: sequences with defined mean/s.d.

July 31, 2013
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Trivial, but useful: sequences with defined mean/s.d.

O.k., the following post may be (mathematically) trivial, but could be somewhat useful for people that do simulations/testing of statistical methods. Let’s say we want to test the dependence of p-values derived from a t-test to a) the ratio of means between two groups, b) the standard deviation or c) the sample size(s) of the

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Exploratory Data Analysis: Combining Histograms and Density Plots to Examine the Distribution of the Ozone Pollution Data from New York in R

Exploratory Data Analysis: Combining Histograms and Density Plots to Examine the Distribution of the Ozone Pollution Data from New York in R

Introduction This is a follow-up post to my recent introduction of histograms.  Previously, I presented the conceptual foundations of histograms and used a histogram to approximate the distribution of the “Ozone” data from the built-in data set “airquality” in R.  Today, I will examine this distribution in more detail by overlaying the histogram with parametric

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The Secrets of Inverse Brogramming, reprise

July 27, 2013
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The Secrets of Inverse Brogramming, reprise

Brogramming is the art of looking good while you write code. Inverse brogramming is a silly term that I’m trying to coin for the opposite, but more important, concept: the art of writing good looking code. At useR2013 I gave a talk on inverse brogramming in R – for those of you who weren’t there

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shinyPsychometric: simulating how experimental choices affect results uncertainty

July 25, 2013
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shinyPsychometric: simulating how experimental choices affect results uncertainty

In the R community, Shiny is trending a lot. Shiny is an R package for developing interactive web applications. I wanted to give it a try, and ended up with an app to simulate the impact of data collection choices on the outcome reliability of a curve fitting process. I wrote the code behind it

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Electricity Usage in a High-rise Condo Complex pt 4

July 24, 2013
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Electricity Usage in a High-rise Condo Complex pt 4

This is the fourth article in the series, where the techiness builds to a crescendo. If this is too statistical/programming geeky for you, the next posting will return to a more investigative and analytical flavor. Last time, we looked at … Continue reading →

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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...

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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

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

July 15, 2013
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Getting Started with Reproducible Research: A chapter from my new book

(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...

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

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

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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

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