1712 search results for "Regression"

Feature Selection 2 – Genetic Boogaloo

May 8, 2013
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Feature Selection 2 – Genetic Boogaloo

Previously, I talked about genetic algorithms (GA) for feature selection and illustrated the algorithm using a modified version of the GA R package and simulated data. The data were simulated with 200 non-informative predictors and 12 linear effects and three non-linear effects. Quadratic discriminant analysis (QDA) was used to model the data. The last set of...

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How to Calculate a Partial Correlation Coefficient in R: An Example with Oxidizing Ammonia to Make Nitric Acid

How to Calculate a Partial Correlation Coefficient in R: An Example with Oxidizing Ammonia to Make Nitric Acid

Introduction Today, I will talk about the math behind calculating partial correlation and illustrate the computation in R with an example involving the oxidation of ammonia to make nitric acid using a built-in data set in R called stackloss.  In a separate post, I will also share an R function that I wrote to estimate partial correlation.

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A pathological glm() problem that doesn’t issue a warning

May 1, 2013
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A pathological glm() problem that doesn’t issue a warning

I know I have already written a lot about technicalities in logistic regression (see for example: How robust is logistic regression? and Newton-Raphson can compute an average). But I just ran into a simple case where R‘s glm() implementation of logistic regression seems to fail without issuing a warning message. Yes the data is a Related posts:

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SAS Big Data Analytics Benchmark (Part One)

April 30, 2013
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by Thomas Dinsmore On April 26, SAS published on its website an undated Technical Paper entitled Big Data Analytics: Benchmarking SAS, R and Mahout. In the paper, the authors (Allison J. Ames, Ralph Abbey and Wayne Thompson) describe a recent project to compare model quality, product completeness and ease of use for two SAS products together with open source...

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A Brief Tour of the Trees and Forests

April 29, 2013
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A Brief Tour of the Trees and Forests

Tree methods such as CART (classification and regression trees) can be used as alternatives to logistic regression. It is a way that can be used to show the probability of being in any hierarchical group. The following is a compilation of many of the key R packages that cover trees and forests.  The goal here

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Forecasting stock returns using ARIMA model with exogenous variable in R

April 28, 2013
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Forecasting stock returns using ARIMA model with exogenous variable in R

Why is it important? Why is it important? India has a lot to achieve in terms of becoming a developed nation from an economic standpoint. An aspect which, in my opinion, is of utmost importance is the formation of structurally sound and robust...

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Revolution Newsletter: April 2013

April 26, 2013
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The most recent edition of the Revolution Newsletter is out. The news section is below, and you can read the full April edition (with highlights from this blog and community events) online. You can subscribe to the Revolution Newsletter to get it monthly via email. Now Available: Revolution R Enterprise 6.2. Released today, this update further enhances the performance,...

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A Call for Context-Aware Measurement

April 25, 2013
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A Call for Context-Aware Measurement

Context awareness seems to be everywhere, and everyone seems to be saying that context matters.  Gartner foresees "a game-changing opportunity" in what it calls context-aware computing.  The title of their report states it best, "Context Shap...

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Announcing Revolution R Enterprise 6.2

April 24, 2013
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Announcing Revolution R Enterprise 6.2

We are pleased to announce that Revolution R Enterprise Release 6.2 is available to new subscribers today. This new software release from Revolution Analytics includes a number of key new features: Support for open source R 2.15.3, the latest stable release of R. Since Release 2.14.2, the R Project has added 89 new features, 11 performance enhancements and 139...

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Disaggregating Annual Losses into Each Quarter

April 23, 2013
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Disaggregating Annual Losses into Each Quarter

In loss forecasting, it is often necessary to disaggregate annual losses into each quarter. The most simple method to convert low frequency to high frequency time series is interpolation, such as the one implemented in EXPAND procedure of SAS/ETS. In the example below, there is a series of annual loss projections from 2013 through 2016.

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