2206 search results for "Regression"

Data Preparation – Part I

October 31, 2013
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Data Preparation – Part I

The R language provides tools for modeling and visualization, but is still an excellent tool for handling/preparing data. As C++ or python, there is some tricks that bring performance, make the code clean or both, but especially with R these choices can have a huge impact on performance and the “size” of your code. A The post Data...

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Finding R in a Hadoop World

October 31, 2013
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Finding R in a Hadoop World

by Joseph Rickert The following is a brief report of all things R encountered in my not quite random, but nevertheless far from determined, walk through the O'Reilly Strata / Hadoop World Conference held this week in NYC. To start off, I had the pleasure of doing a 9:00 AM Monday morning joint tutorial with Antonio Piccolboni, the principal...

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Visual interpretation of interaction terms in linear models with ggplot #rstats

October 31, 2013
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Visual interpretation of interaction terms in linear models with ggplot #rstats

I haven’t used interaction terms in (generalized) linear model quite often yet. However, recently I have had some situations where I tried to compute regression models with interaction terms and was wondering how to interprete the results. Just looking at the estimates won’t help much in such cases. One approach used by some people is

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More significant? so what…

October 30, 2013
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More significant? so what…

Following my non-life insurance class, this morning, I had an interesting question from a student, that I will try to illustrate, and reformulate as accurately as possible. Consider a simple regression model, with one variable of interest, and one possible explanatory variable. Assume that we have two possible models, with the following output (yes, I do hide interesting parts...

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Call them what you will

October 28, 2013
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I’ve been playing around with the R package texreg for creating combined regression tables for multiple models. It’s not the only package to do that – see here for a review – but it’s often handy to be able to generate both ascii art, latex, and html versions of the same table using almost identical

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Let’s have a "party" and tear this place "rpart"!

October 27, 2013
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Let’s have a "party" and tear this place "rpart"!

For many problems, classification and regression trees can be a simple and elegant solution, assuming you know their well-documented strengths and weaknesses.  I first explored their use several years ago with JMP, which is easy to use.  If y...

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The Basics of Encoding Categorical Data for Predictive Models

October 23, 2013
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The Basics of Encoding Categorical Data for Predictive Models

Thomas Yokota asked a very straight-forward question about encodings for categorical predictors: "Is it bad to feed it non-numerical data such as factors?" As usual, I will try to make my answer as complex as possible. (I've heard the old wives tale that eskimos have 180 different words in their language for snow. I'm starting to think that statisticians have...

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New R package: scholar

October 23, 2013
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New R package: scholar

My new R package, scholar, has just been posted on CRAN. The scholar package provides functions to extract citation data from Google Scholar. In addition to retrieving basic information about a single scholar, the package also allows you to compare multiple scholars and predict future h-index values. There’s a full guide on Github (along

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GLM, non-linearity and heteroscedasticity

October 22, 2013
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GLM, non-linearity and heteroscedasticity

Last week in non-life insurance course, we’ve seen the theory of the Generalized Linear Models, emphasizing the two important components the link function (which is actually the key component in predictive modeling) the distribution, or the variance function Just to illustrate, consider my favorite dataset ­lin.mod = lm(dist~speed,data=cars) A linear model means here where the residuals are assumed to be...

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An introduction to Econometrics, using R

October 22, 2013
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If your econometrics is a bit rusty and you're also looking to learn the R language, you can kill two birds with one stone with Introductory Econometrics using Quandl and R. The first three parts of this seven-part tutorial introduces the basics of regression analysis, while the remaining sections provide R code you can try yourself to reproduce econometric...

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