# 2378 search results for "regression"

## 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...

## 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

## 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...

## 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...

## 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...

## Estimating the Decay Rate and the Half-Life of DDT in Trout – Applying Simple Linear Regression with Logarithmic Transformation

This blog post uses a function and a script written in R that were displayed in an earlier blog post. Introduction This is the second of a series of blog posts about simple linear regression; the first was written recently on some conceptual nuances and subtleties about this model.  In this blog post, I will use

## My Own R Function and Script for Simple Linear Regression – An Illustration with Exponential Decay of DDT in Trout

Here is the function that I wrote for doing simple linear regression, as alluded to in my blog post about simple linear regression on log-transformed data on the decay of DDT concentration in trout in Lake Michigan.  My goal was to replicate the 4 columns of the output from applying summary() to the output of lm().

## Using Norms to Understand Linear Regression

March 22, 2013
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Introduction In my last post, I described how we can derive modes, medians and means as three natural solutions to the problem of summarizing a list of numbers, \((x_1, x_2, \ldots, x_n)\), using a single number, \(s\). In particular, we measured the quality of different potential summaries in three different ways, which led us to

## Veterinary Epidemiologic Research: GLM (part 4) – Exact and Conditional Logistic Regressions

March 22, 2013
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Next topic on logistic regression: the exact and the conditional logistic regressions. Exact logistic regression When the dataset is very small or severely unbalanced, maximum likelihood estimates of coefficients may be biased. An alternative is to use exact logistic regression, available in R with the elrm package. Its syntax is based on an events/trials formulation.

## Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)

March 19, 2013
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$Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)$

Third part on logistic regression (first here, second here). Two steps in assessing the fit of the model: first is to determine if the model fits using summary measures of goodness of fit or by assessing the predictive ability of the model; second is to deterime if there’s any observations that do not fit the