Blog Archives

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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%EXPORT_TO_R SAS Macro Code

May 6, 2011
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%EXPORT_TO_R SAS Macro Code

The SAS Analysis blog post 'A macro calls R in SAS for paneled 3d plotting' influenced my macro coding.   The following macro call: %EXPORT_TO_R(DATA = YOURDATA)  exports the SAS data set 'YOURDATA' as .csv and produces the R code for se...

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An Intuitive Approach to ROC Curves (with SAS & R)

May 6, 2011
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An Intuitive Approach to ROC Curves  (with SAS & R)

I developed the following schematic (with annotations) based on supporting documents (link) from the article cited below. The authors used R for their work. The ROC curve in my schematic was output from PROC LOGISTIC in SAS, the scatterplot with m...

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