1620 search results for "Regression"

More 3D Graphics (rgl) for Classification with Local Logistic Regression and Kernel Density Estimates (from The Elements of Statistical Learning)

February 7, 2015
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More 3D Graphics (rgl) for Classification with Local Logistic Regression and Kernel Density Estimates (from The Elements of Statistical Learning)

This post builds on a previous post, but can be read and understood independently. As part of my course on statistical learning, we created 3D graphics to foster a more intuitive understanding of the various methods that are used to relax the assumption of linearity (in the predictors) in regression and classification methods. The authors

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Inequalities and Quantile Regression

February 6, 2015
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Inequalities and Quantile Regression

In the course on inequality measure, we've seen how to compute various (standard) inequality indices, based on some sample of incomes (that can be binned, in various categories). On Thursday, we discussed the fact that incomes can be related to different variables (e.g. experience), and that comparing income inequalities between coutries can be biased, if they have very different...

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Some 3D Graphics (rgl) for Classification with Splines and Logistic Regression (from The Elements of Statistical Learning)

February 1, 2015
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Some 3D Graphics (rgl) for Classification with Splines and Logistic Regression (from The Elements of Statistical Learning)

This semester I'm teaching from Hastie, Tibshirani, and Friedman's book, The Elements of Statistical Learning, 2nd Edition. The authors provide a Mixture Simulation data set that has two continuous predictors and a binary outcome. This data is used to demonstrate classification procedures by plotting classification boundaries in the two predictors. For example, the figure below

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SAS PROC MCMC example in R: Logistic Regression Random-Effects Model

January 18, 2015
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In this post I will run SAS example Logistic Regression Random-Effects Model in four R based solutions; Jags, STAN, MCMCpack and LaplacesDemon. To quote the SAS manual: 'The data are taken from Crowder (1978). The Seeds data set is a 2 x 2 fa...

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Introducing: Orthogonal Nonlinear Least-Squares Regression in R

January 17, 2015
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Introducing: Orthogonal Nonlinear Least-Squares Regression in R

With this post I want to introduce my newly bred ‘onls’ package which conducts Orthogonal Nonlinear Least-Squares Regression (ONLS): http://cran.r-project.org/web/packages/onls/index.html. Orthogonal nonlinear least squares (ONLS) is a not so frequently applied and maybe overlooked regression technique that comes into question when one encounters an “error in variables” problem. While classical nonlinear least squares (NLS) aims

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Regression Solutions Available

January 8, 2015
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Regression Solutions Available

The github page for the APM exercises has been updated with three new files for Chapters 6-8 (the section on regression). The classifications section is in-progress. Here's one of our fancy-pants graphs:

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Classification and Regression Trees using R

January 7, 2015
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Recursive partitioning is a fundamental tool in data mining. It helps us explore the structure of a set of data, while developing easy to visualize decision rules for predicting a categorical (classification tree) or continuous (regression tree) outcome. Classification and regression trees can be generated through the rpart package. The post Classification and Regression Trees using R appeared first on...

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SAS PROC MCMC in R: Nonlinear Poisson Regression Models

December 6, 2014
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SAS PROC MCMC in R: Nonlinear Poisson Regression Models

In exercise 61.1 the problem is that the model has bad mixing. In the SAS manual the mixing is demonstrated after which a modified distribution is used to fix the model.In this post the same problem is tackled in R; MCMCpack, RJags, RStan and LaplaceDemon. MCMCpack has quite some mixing problems, RStan seems to do best.DataTo quote the SAS...

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Performing Logistic Regression in R and SAS

Performing Logistic Regression in R and SAS

Introduction My statistics education focused a lot on normal linear least-squares regression, and I was even told by a professor in an introductory statistics class that 95% of statistical consulting can be done with knowledge learned up to and including a course in linear regression.  Unfortunately, that advice has turned out to vastly underestimate the

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Interpreting regression coefficient in R

November 23, 2014
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Interpreting regression coefficient in R

Linear models are a very simple statistical techniques and is often (if not always) a useful start for more complex analysis. It is however not so straightforward to understand what the regression coefficient means even in the most simple case when there are no interactions in the model. If we are not only fishing for

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