2131 search results for "Regression"

Theil’s Blus Residuals and R Tools for Testing and Removing Autocorrelation and Heteroscedasticity

March 28, 2014
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Guest post by Hrishikesh (Rick) D. Vinod, Professor of Economics, Fordham University. Theil (1968) proposed a transformation of regression residuals so that they are best, unbiased, linear, scalar (BLUS). No R code is available to implement them. I am providing the detailed description of the properties of BLUS residuals to the uninitiated and code. The matrix algebra itself is...

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Why use R? Five reasons.

March 27, 2014
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Why use R? Five reasons.

In this post I will go through 5 reasons: zero cost, crazy popularity, awesome power, dazzling flexibility, and mind-blowing support. I believe R is the best statistical programming language to learn. As a blogger who has contributed over 150 posts in Stata and over 100 in R I have extensive experience with both a proprietary statistical programming language...

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Bayesian Data Analysis [BDA3]

March 27, 2014
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Bayesian Data Analysis [BDA3]

Andrew Gelman and his coauthors, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin, have now published the latest edition of their book Bayesian Data Analysis. David and Aki are newcomers to the authors’ list, with an extended section on non-linear and non-parametric models. I have been asked by Sam Behseta to write

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R User Group Activity for Q1 2014

March 27, 2014
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R User Group Activity for Q1 2014

by Joseph Rickert Worldwide R user group activity for the first Quarter of 2014 appears to be way up compared to previous years as the following plot shows. The plot was built by counting the meetings on Revolution Analytics R Community Calendar. R users continue to value the live, in person events and face-to-face meetings with their peers. Moreover,...

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MCMC for Econometrics Students – Part IV

March 26, 2014
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MCMC for Econometrics Students – Part IV

This is the fourth in a sequence of posts designed to introduce econometrics students to the use of Markov Chain Monte Carlo (MCMC, or MC2) simulation methods for Bayesian inference. The first three posts can be found here, here, and here, and I'll assume that you've read them already. The emphasis throughout is on the...

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A Thumbnail History of Ensemble Methods

March 25, 2014
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A Thumbnail History of Ensemble Methods

By Mike Bowles Ensemble methods are the backbone of machine learning techniques. However, it can be a daunting subject for someone approaching it for the first time, so we asked Mike Bowles, machine learning expert and serial entrepreneur to provide some context. Ensemble Methods are among the most powerful and easiest to use of predictive analytics algorithms and R...

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Wright Map Tutorial – Part 3

March 25, 2014
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Wright Map Tutorial – Part 3

In this part of the tutorial, we’ll show how to load ConQuest output to make a CQmodel object and then WrightMaps. We’ll also show how to turn deltas into thresholds. All the example files here are available in the /inst/extdata folder of the github. If you download the latest version of the package, they should be in a folder...

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Wright Map Tutorial – Part 3

March 25, 2014
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Wright Map Tutorial – Part 3

In this part of the tutorial, we’ll show how to load ConQuest output to make a CQmodel object and then WrightMaps. We’ll also show how to turn deltas into thresholds. All the example files here are available in the /inst/extdata folder of the github. If you download the latest version of the package, they should be in a folder...

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Stochastic search variable selection in JAGS

March 22, 2014
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Stochastic search variable selection in JAGS

Stochastic search variable selection (SSVS) identifies promising subsets of multiple regression covariates via Gibbs sampling (George and McCulloch 1993). Here’s a short SSVS demo with JAGS and R. Assume we have a multiple regression problem: We suspect only a subset of the elements of $\boldsymbol{\beta}$ are non-zero, i.e. some of the covariates have no effect. Assume $\boldsymbol{\beta}$...

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Stochastic search variable selection in JAGS

March 22, 2014
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Stochastic search variable selection in JAGS

Stochastic search variable selection (SSVS) identifies promising subsets of multiple regression covariates via Gibbs sampling (George and McCulloch 1993). Here’s a short SSVS demo with JAGS and R. Assume we have a multiple regression problem: We suspect only a subset of the elements of $boldsymbol{beta}$ are non-zero, i.e. some of the covariates have no effect. Assume $boldsymbol{beta}$ arises from...

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