1042 search results for "regression"

R Bootcamp Materials!

February 24, 2013
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R Bootcamp Materials!

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...

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the BUGS Book [guest post]

February 24, 2013
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the BUGS Book [guest post]

(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

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A slightly different introduction to R, part IV

February 21, 2013
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A slightly different introduction to R, part IV

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

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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,...

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When SAP HANA met R – What’s new?

February 18, 2013
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When SAP HANA met R – What’s new?

Since I wrote my blog When SAP HANA met R - First kiss I had received a lot of nice feedback...and one those feedbacks was..."What's new?"...Well...as you might now SAP HANA works with R by using Rserve, a package that allows communication to an R Serv...

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Automatic spatial interpolation with R: the automap package

February 17, 2013
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Automatic spatial interpolation with R: the automap package

In case of continuously collected data, e.g. observations from a monitoring network, spatial interpolation of this data cannot be done manually. Instead, the interpolation should be done automatically. To achieve this goal, I developed the automap package. automap builds on

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Veterinary Epidemiologic Research: Linear Regression

February 14, 2013
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Veterinary Epidemiologic Research: Linear Regression

This post will describe linear regression as from the book Veterinary Epidemiologic Research, describing the examples provided with R. Regression analysis is used for modeling the relationship between a single variable Y (the outcome, or dependent variable) measured on a continuous or near-continuous scale and one or more predictor (independent or explanatory variable), X. If

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Version 1.0 of multilevelPSA Available on CRAN

February 14, 2013
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Version 1.0 of multilevelPSA Available on CRAN

Version 1.0 of multilevelPSA has been released to CRAN. The multilevelPSA package provides functions to estimate and visualize propensity score models with multilevel, or clustered, data. The graphics are an extension of PSAgraphics package by Helmreich and Pruzek. The example below will investigate the differences between private and public school internationally using the Programme of International Student Assessment...

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Large claims, and ratemaking

February 13, 2013
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Large claims, and ratemaking

During the course, we have seen that it is natural to assume that not only the individual claims frequency can be explained by some covariates, but individual costs too. Of course, appropriate families should be considered to model the distribution of the cost , given some covariates .Here is the dataset we’ll use, > sinistre=read.table("http://freakonometrics.free.fr/sinistreACT2040.txt", + header=TRUE,sep=";") > sinistres=sinistre...

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Exposure with binomial responses

February 9, 2013
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Exposure with binomial responses

Last week, we’ve seen how to take into account the exposure to compute nonparametric estimators of several quantities (empirical means, and empirical variances) incorporating exposure. Let us see what can be done if we want to model a binomial response. The model here is the following: , the number of claims  on the period  is unobserved the number of...

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