Blog Archives

Using SNA in Predictive Modeling

April 10, 2012
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Using SNA in Predictive Modeling

In a previous post, I described the basics of social network analysis. I plan to extend that example here with an application in predictive analytics. Let's suppose we have the following network (visualized in R)Suppose we have used the igraph package ...

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An Introduction to Social Network Analysis with R and NetDraw

April 10, 2012
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With the rise in the use of social media, data related to social networks is ripe for analysis using techniques from social network analysis and graph theory. According to International Network for Social Network Analysis, ‘Social network analysis is...

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Time Series Intervention Analysis wih R and SAS

January 21, 2012
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Time Series Intervention Analysis wih R and SAS

In a previous post, I worked through the theory behind intervention analysis. In his time series course, University of Georgia political science professor Jamie Monogan demonstrates how to implement intervention analysis in R.  The following examp...

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Regression via Gradient Descent in R

November 27, 2011
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Regression via Gradient Descent in R

In a previous post I derived the least squares estimators using basic calculus, algebra, and arithmetic, and also showed how the same results can be achieved using the canned functions in SAS and R or via the matrix programming capabilities offered by ...

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Basic Econometrics in R and SAS

November 27, 2011
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Basic Econometrics in R and SAS

Regression Basicsy= b0 + b1 *X  ‘regression line we want to fit’The method of least squares minimizes the squared distance between the line ‘y’ andindividual data observations yi. That is minimize: ∑ ei2 = ∑ (yi - b0 -  b1 Xi...

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Gradient Descent in R

November 27, 2011
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Gradient Descent in R

In a previous post I discussed the concept of gradient descent.  Given some recent work in the online machine learning course offered at Stanford,  I'm going to extend that discussion with an actual example using R-code  (the actual code...

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Bayesian Models with Censored Data: A comparison of OLS, tobit and bayesian models

September 17, 2011
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Bayesian Models with Censored Data: A comparison of OLS, tobit and bayesian models

The following R code models a censored dependent variable (in this case academic aptitude) using a traditional least squares, tobit, and Bayesian approaches.  As depicted below, the OLS estimates (blue) for censored data are inconsistent and will ...

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Elements of Bayesian Econometrics

September 16, 2011
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Elements of Bayesian Econometrics

 posterior = (likelihood x prior) / integrated likelihoodThe combination of a prior distribution and a likelihood function is utilized to produce a posterior distribution.  Incorporating information from both the prior distribution and the likelihood function leads to a reduction in variance and an improved estimator. As n→...

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QTL Analysis in R

August 13, 2011
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QTL Analysis in R

See also: Part 1: QTL Analysis and Quantitative Genetics  Part 2: Statistical Methods for QTL Analysis The 'qtl' package in R allows you to implement QTL analysis using the methods I've previously discussed. The code below is adapted from Broman...

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R Program Documentation Template

August 13, 2011
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R Program Documentation Template

# ------------------------------------------------------------------ # |PROGRAM NAME: # |DATE: # |CREATED BY: MATT BOGARD # |PROJECT FILE: # |---------------------------------------------------------------- # | PURPOSE: ...

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