# 1036 search results for "regression"

## Moving Average Representation of VAR

March 10, 2013
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A vector autoregression (VAR) process can be represented in a couple of ways. The usual form is as follows:     The above (AR process) is what we often see and use in practice. However, I recently see more and … Continue reading

## Veterinary Epidemiologic Research: Linear Regression Part 2 – Checking assumptions

March 6, 2013
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We continue on the linear regression chapter the book Veterinary Epidemiologic Research. Using same data as last post and running example 14.12: Now we can create some plots to assess the major assumptions of linear regression. First, let’s have a look at homoscedasticity, or constant variance of residuals. You can run a statistical test, the

## Visualizing neural networks from the nnet package

March 4, 2013
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Neural networks have received a lot of attention for their abilities to ‘learn’ relationships among variables. They represent an innovative technique for model fitting that doesn’t rely on conventional assumptions necessary for standard models and they can also quite effectively handle multivariate response data. A neural network model is very similar to a non-linear regression

## Tools for making a paper

March 1, 2013
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Since it seems to be the fashion, here’s a post about how I make my academic papers. Actually, who am I trying to kid? This is also about how I make slides, letters, memos and “Back in 10 minutes” signs to pin on the door. Nevertheless it’s for making academic papers that I’m going to

## How to make a scientific result disappear

February 27, 2013
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$How to make a scientific result disappear$

Nathan Danneman (a co-author and one of my graduate students from Emory) recently sent me a New Yorker article from 2010 about the “decline effect,” the tendency for initially promising scientific results to get smaller upon replication. Wikipedia can summarize the phenomenon as well as I can: In his article, Lehrer gives several examples where

## R Bootcamp Materials!

February 24, 2013
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Learn about ColoRs in R!Analyze model results with custom functions.Good and Bad GraphicsTo train new employees at the Wisconsin Department of Public Instruction, I have developed a 2-3 day series of training modules on how to get work done in R. These...

## the BUGS Book [guest post]

February 24, 2013
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(My colleague Jean-Louis Fouley, now at I3M, Montpellier, kindly agreed to write a review on the BUGS book for CHANCE. Here is the review, en avant-première! Watch out, it is fairly long and exhaustive! References will be available in the published version. The additions of book covers with BUGS in the title and of the corresponding

## A slightly different introduction to R, part IV

February 21, 2013
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Now, after reading in data, making plots and organising commands with scripts and Sweave, we’re ready to do some numerical data analysis. If you’re following this introduction, you’ve probably been waiting for this moment, but I really think it’s a good idea to start with graphics and scripting before statistical calculations. We’ll use the silly

## Working with R2MLwiN Part 2

February 19, 2013
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## Specifying the model

This is the second part of a series of notes demonstrating use of the R package, R2MLwiN, an R command interface to the multilevel modelling software package, MLwiN (see the MLwiN site for getting access to MLwiN). The first set of notes showed how to get started with R2MLwiN. In these notes,...