1570 search results for "regression"

SAS PROC MCMC in R: Nonlinear Poisson Regression Models

December 6, 2014
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SAS PROC MCMC in R: Nonlinear Poisson Regression Models

In exercise 61.1 the problem is that the model has bad mixing. In the SAS manual the mixing is demonstrated after which a modified distribution is used to fix the model.In this post the same problem is tackled in R; MCMCpack, RJags, RStan and LaplaceDemon. MCMCpack has quite some mixing problems, RStan seems to do best.DataTo quote the SAS...

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Performing Logistic Regression in R and SAS

Performing Logistic Regression in R and SAS

Introduction My statistics education focused a lot on normal linear least-squares regression, and I was even told by a professor in an introductory statistics class that 95% of statistical consulting can be done with knowledge learned up to and including a course in linear regression.  Unfortunately, that advice has turned out to vastly underestimate the

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Interpreting regression coefficient in R

November 23, 2014
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Interpreting regression coefficient in R

Linear models are a very simple statistical techniques and is often (if not always) a useful start for more complex analysis. It is however not so straightforward to understand what the regression coefficient means even in the most simple case when there are no interactions in the model. If we are not only fishing for

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SAS PROC MCMC example in R; Poisson Regression

November 16, 2014
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In this post I will try to copy the calculations of SAS's PROC MCMC example 61.5 (Poisson Regression) into the various R solutions. In this post Jags, RStan, MCMCpack, LaplacesDemon solutions are shown. Compared to the first post in this series, rcppbugs and mcmc are not used. Rcppbugs has no poisson distribution and while I know how to...

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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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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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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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