# 2394 search results for "regression"

## Regression regularization example

May 31, 2013
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Recently I needed a simple example showing when application of regularization in regression is worthwhile. Here is the code I came up with (along with basic application of parallelization of code execution). Assume you have 60 observations and 50 expla...

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## Bayesian model II regression

May 27, 2013
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Regression is a mainstay of ecological and evolutionary data analysis. For example, a disease ecologist may use body size (e.g. a weight from a scale with measurement error) to predict infection. Classical linear regression assumes no error in covariates; they are known exactly. This is rarely the case in ecology, and ignoring error in covariates can bias regression coefficient...

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## When Does the Kinetic Theory of Gases Fail? Examining its Postulates with Assistance from Simple Linear Regression in R

$When Does the Kinetic Theory of Gases Fail? Examining its Postulates with Assistance from Simple Linear Regression in R$

Introduction The Ideal Gas Law, , is a very simple yet useful relationship that describes the behaviours of many gases pretty well in many situations.  It is “Ideal” because it makes some assumptions about gas particles that make the math and the physics easy to work with; in fact, the simplicity that arises from these

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## Veterinary Epidemiologic Research: Count and Rate Data – Poisson Regression and Risk Ratios

May 10, 2013
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As noted on paragraph 18.4.1 of the book Veterinary Epidemiologic Research, logistic regression is widely used for binary data, with the estimates reported as odds ratios (OR). If it’s appropriate for case-control studies, risk ratios (RR) are preferred for cohort studies as RR provides estimates of probabilities directly. Moreover, it is often forgotten the assumption

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## Poisson regression on non-integers

May 7, 2013
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In the course on claims reserving techniques, I did mention the use of Poisson regression, even if incremental payments were not integers. For instance, we did consider incremental triangles > source("http://perso.univ-rennes1.fr/arthur.charpentier/bases.R") > INC=PAID > INC=PAID-PAID > INC 3209 1163 39 17 7 21 3367 1292 37 24 10 NA 3871...

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## All About Spherically Distributed Regression Errors

May 2, 2013
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This post is based on a handout that I use for one of my courses, and it relates to the usual linear regression model,                                   y = Xβ + ε In our list of standard assumptions about the error term in this linear multiple regression...

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## Veterinary Epidemiologic Research: Count and Rate Data – Poisson & Negative Binomial Regressions

April 22, 2013
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Still going through the book Veterinary Epidemiologic Research and today it’s chapter 18, modelling count and rate data. I’ll have a look at Poisson and negative binomial regressions in R. We use count regression when the outcome we are measuring is a count of number of times an event occurs in an individual or group

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## Stepwise Regression for Big Data with RevoScaleR

April 11, 2013
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by Joseph Rickert In a recent blog post, Revolution's Thomas Dinsmore announced stepwise regression for big data as a new feature of Revolution R Enterprise 6.2 that is scheduled for general availability later this month. Today, I would like to provide a simple example of doing stepwise regression with rxLinMod() (the RevoScaleR analog of lm()), using a 100,000 row...

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## Estimating continuous piecewise linear regression

April 2, 2013
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When talking about smoothing splines a simple point to start with is a continuous piecewise linear regression with fixed knots. I did not find any simple example showing how to estimate the it in GNU R so I have created a little snippet that does the j...

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## What’s New in 6.2: Stepwise Regression for Big Data

March 26, 2013
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by Thomas Dinsmore This is the third in a series of posts highlighting new features in Revolution R Enterprise Release 6.2, which is scheduled for General Availability April 22. This week's post features our new Stepwise Regression capability. The Stepwise process starts with a specified model and then sequentially adds into or removes from the model the variable that...

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