# 1237 search results for "regression"

## Getting Started with Mixed Effect Models in R

November 25, 2013
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Getting Started with Multilevel Modeling in R Getting Started with Multilevel Modeling in R Jared E. Knowles Introduction Analysts dealing with grouped data and complex hierarchical structures in their data ranging from measurements nested within participants, to counties nested within states or students nested within classrooms often find themselves...

## Book Review: Applied Predictive Modeling by Max Kuhn and Kjell Johnson

November 24, 2013
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This is a gem of a book.From the introduction: We intend this work to be a practitioner’s guide to the predictive modeling process and a place where one can come to learn about the approach and to gain intuition about the many commonly used and modern, powerful models. …it was our goal to be as hands-on as possible, enabling the readers...

## R and Bayesian Statistics

November 21, 2013
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by Joseph Rickert Drew Linzer, the Bayesian statistician who attracted considerable attention last year with his spot-on, R-based forecast of the 2012 presidential election, recently gave a tutorial on Bayesian statistics to the Bay Area useR Group (BARUG). Drew covered quite a bit of ground running R code that showed how to make use of WinBugs, JAGS and Stan,...

## How to format plots for publication using ggplot2 (with some help from Inkscape)

November 20, 2013
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The following is the code from a presentation made by Rosemary Hartman to the Davis R Users’ Group. I’ve run the code through the spin function in knitr to produce this post. Download the script to walk through here. First, make your plot. I am going to use the data already in R about sleep habits...

## Predicting optimal of iterations and completion time for GBM

November 20, 2013
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When choosing the hyperparameters for Generalized Boosted Regression Models, two important choices are shrinkage and the number of trees. Generally a smaller shrinkage with more trees produces a better model, but the modeling time significantly increases. Building a model with too many trees that are heavily cut back by cross validation wastes time, while building a model...

## On the use of marginal posteriors in marginal likelihood estimation via importance-sampling

November 19, 2013
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Perrakis, Ntzoufras, and Tsionas just arXived a paper on marginal likelihood (evidence) approximation (with the above title). The idea behind the paper is to base importance sampling for the evidence on simulations from the product of the (block) marginal posterior distributions. Those simulations can be directly derived from an MCMC output by randomly permuting the

## Binomial regression model

November 18, 2013
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$Y_i\sim\mathcal{B}(p(\boldsymbol{X_i}))$

Most of the time, when we introduce binomial models, such as the logistic or probit models, we discuss only Bernoulli variables, . This year (actually also the year before), I discuss extensions to multinomial regressions, where  is a function on some simplex. The multinomial logistic model was mention here. The idea is to consider, for instance with three possible classes the following...

## Simulation (is where it’s happening)

November 18, 2013
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Jim Silverton wrote to the Allstat mailing list recently: “Hi, Anyone up for a challenge? Suppose we have random variables that are random points on the surface of a sphere. What is the probability that the tetrahedron made by joining these … Continue reading →

## Some Options for Testing Tables

November 18, 2013
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Contingency tables are a very good way to summarize discrete data.  They are quite easy to construct and reasonably easy to understand. However, there are many nuances with tables and care should be taken when making conclusions related to the data. Here are just a few thoughts on the topic. Dealing with sparse data On