# Monthly Archives: August 2013

## Using a GBM for Classification in R

August 26, 2013
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I discuss some advantages of Generalized Boosted Models over logistic regression and discriminant analysis and demonstrate how to use a GBM for binary classification (predicting whether an event occurs or not). Using a GBM for Classification in R from...

## Nonparametric (local polynomial) regression in R

August 26, 2013
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Local polynomial regression models can be used as a more flexible alternative to linear regression. However, the nonparametric regression models are slightly more difficult to estimate and interpret than linear regression. This video explains almost ev...

## Estimation, prediction, and evaluation of logistic regression models

August 26, 2013
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I provide an introduction to using logistic regression for prediction (binary classification) using the Titanic data competition from www.Kaggle.com as an example. I use models to predict in missing data, estimate a logistic regression model on a trai...

## R Video Blog! 2013-08-26 08:39:00

August 26, 2013
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For more R resources, check out R-Bloggers! I seriously learn something every day from this site.

## International Summer School on Social Network Analysis: Introduction and Methods of Analysis in R

August 26, 2013
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As mentioned in a previous post, Alex Hanna and I had the opportunity to teach last week at the Higher School of Economic’s International Social Network Analysis Summer School in St. Petersburg.  While last year’s workshop emphasized smaller social networks, this year’s workshop focused on online networks.  For my part, I provided an introductory lecture to… Continue reading →

## Estimation and simulation of the geometric Ornstein-Uhlenbeck process

August 26, 2013
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The Ornstein-Uhlenbeck process is mean reverting process commonly used to model commodity prices. I demonstrate how to estimate the process using a set of price data and provide a function for simulation.

## R vs Python: Practical Data Analysis (Nonlinear Regression)

August 26, 2013
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I’ve written a few previous posts comparing R to Python in terms of symbolic math, optimization, and bootstrapping. All of these posts were pretty popular. The last one especially. Many of the commenters brought up the fact that R, while … Continue reading →

## predictNLS (Part 2, Taylor approximation): confidence intervals for ‘nls’ models

August 26, 2013
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$predictNLS (Part 2, Taylor approximation): confidence intervals for ‘nls’ models$

Initial Remark: Reload this page if formulas don’t display well! As promised, here is the second part on how to obtain confidence intervals for fitted values obtained from nonlinear regression via nls or nlsLM (package ‘minpack.lm’). I covered a Monte Carlo approach in http://rmazing.wordpress.com/2013/08/14/predictnls-part-1-monte-carlo-simulation-confidence-intervals-for-nls-models/, but here we will take a different approach: First- and second-order

## Changeability of Value at Risk estimators

August 26, 2013
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How does Value at Risk change through time for the same portfolio? Previously There has been a number of posts on Value at Risk, including a basic introduction to Value at Risk and Expected Shortfall. The components garch model was also described. Issue The historical method for Value at Risk is by far the most commonly … Continue reading...

## Using JavaScript visualization libraries with R

August 26, 2013
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This is a short tutorial on knitr/markdown and JS visualization packages googleVis and rCharts. With these packages you can create web pages with interactive visualizations just using R. This will require minimal or no knowledge of HTML ...