2130 search results for "regression"

Estimating a Beta Regression with The Variable Dispersion in R

October 19, 2014
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Estimating a Beta Regression with The Variable Dispersion in R

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Regression Analysis using R

October 4, 2014
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Regression Analysis using R

What is a Prediction Problem?A business problem which involves predicting future events by extracting patterns in the historical data. Prediction problems are solved using Statistical techniques, mathematical models or machine learning techniques.For example: Forecasting stock price for the next week, predicting which football team wins the world cup, etc.What is Regression analysis, where is it applicable?While dealing with...

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Structured simulation of regression models – simReg package.

September 30, 2014
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I'd like to introduce a package that simulates regression models. This includes both single level and multilevel (i.e. hierarchical or linear mixed) models up to two levels of nesting. The package produces a unified framework to simulate all types of c...

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Structured simulation of regression models – simReg package.

September 30, 2014
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I'd like to introduce a package that simulates regression models. This includes both single level and multilevel (i.e. hierarchical or linear mixed) models up to two levels of nesting. The package produces a unified framework to simulate all types of c...

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Implementing an EM Algorithm for Probit Regressions

September 30, 2014
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Implementing an EM Algorithm for Probit Regressions

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...

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Implementing an EM Algorithm for Probit Regressions

September 30, 2014
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Implementing an EM Algorithm for Probit Regressions

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...

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

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....

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

September 24, 2014
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Adding Google Drive Times and Distance Coefficients to Regression Models with ggmap and sp

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

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Generalized Double Pareto Priors for Regression

September 10, 2014
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Generalized Double Pareto Priors for Regression

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

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

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

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