# 2071 search results for "regression"

## Implementing an EM Algorithm for Probit Regressions

September 30, 2014
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Users new to the Rcpp family of functionality are often impressed with the performance gains that can be realized, but struggle to see how to approach their own computational problems. Many of the most impressive performance gains are demonstrated with seemingly advanced statistical methods, advanced C++–related constructs, or both. Even when users are able to understand how various demonstrated features operate in isolation, examples...

## RMOA package for running streaming classifcation & regression models now at CRAN

Last week, we released the RMOA package at CRAN (http://cran.r-project.org/web/packages/RMOA). It is an R package to allow building streaming classification and regression models on top of MOA. MOA is the acronym of 'Massive Online Analysis' and it is the most popular open source framework for data stream mining which is being developed at the University of Waikato: http://moa.cms.waikato.ac.nz....

## Adding Google Drive Times and Distance Coefficients to Regression Models with ggmap and sp

September 24, 2014
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Space, a wise man once said, is the final frontier. Not the Buzz Alrdin/Light Year, Neil deGrasse Tyson kind (but seriously, have you seen Cosmos?). Geographic space. Distances have been finding their way into metrics since the cavemen (probably). GIS seem to make nearly every science way more fun…and accurate! Most of my research deals with

## Generalized Double Pareto Priors for Regression

September 10, 2014
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This post is a review of the “GENERALIZED DOUBLE PARETO SHRINKAGE” Statistica Sinica (2012) paper by Armagan, Dunson and Lee. Consider the regression model (Y=Xbeta+varepsilon) where we put a generalized double pareto distribution as the prior on the regression coefficients (beta). The GDP distribution has density \$\$begin{equation} f(beta|xi,alpha)=frac{1}{2xi}left( 1+frac{|beta|}{alphaxi} right)^{-(alpha+1)}. label{} end{equation}\$\$ GDP as Scale The post

## Interactive visualization of non-linear logistic regression decision boundaries with Shiny

(skip to the shiny app) Model building is very often an iterative process that involves multiple steps of choosing an algorithm and hyperparameters, evaluating that model / cross validation, and optimizing the hyperparameters. I find a great aid in this process, for classification tasks, is not only to keep track of the accuracy across models, »more

## Example 2014.7: Simulate logistic regression with an interaction

June 24, 2014
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Reader Annisa Mike asked in a comment on an early post about power calculation for logistic regression with an interaction. This is a topic that has come up with increasing frequency in grant proposals and article submissions. We'll begin by showing how to simulate data with the interaction, and in our next post...

## Advanced Regression Analysis: How To Print All Best Models ?

May 13, 2014
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In this post, I am about to explain you simple way to find as many best possible regression models you want, from any given predictors dataset. I am going to show you a method, along with code, where you can … Continue reading →

## Stacking Regressions: Latex Tables with R and stargazer

May 3, 2014
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In my paper on the impact of the shale oil and gas boom in the US, I run various instrumental variables specifications. For these, it is nice to stack the regression results one on the other – in particular, to have one row for the IV results, one row for the Reduced Form and maybe

## Example of linear regression and regularization in R

April 28, 2014
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When getting started in machine learning, it's often helpful to see a worked example of a real-world problem from start to finish. But it can be hard to find an example with the "right" level of complexity for a novice. Here's what I look for: uses r...

## Use of freqparcoord for Regression Diagnostics

April 14, 2014
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This is the third in my series of three posts on my package freqparcoord with Yingkang Xie. (My next post after this will show how to use R to explore one of my favorite examples of “what can go wrong” in statistics.) Here is a very brief review of my previous posts regarding freqparcoord. A