1489 search results for "regression"

Another view of ordinary regression

July 8, 2013
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Another view of ordinary regression

This is something I’ve been meaning to write for ages. My formal training for most things is limited. Like a lot of folks, I’m an autodidact. This is good in that I’m always learning and always studying those things that I enjoy. At the same time, it means that I take in information in a

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integral priors for binomial regression

July 1, 2013
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integral priors for binomial regression

Diego Salmerón and Juan Antonio Cano from Murcia, Spain (check the movie linked to the above photograph!), kindly included me in their recent integral prior paper, even though I mainly provided (constructive) criticism. The paper has just been arXived. A few years ago (2008 to be precise), we wrote together an integral prior paper, published

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Prototyping A General Regression Neural Network with SAS

June 22, 2013
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Prototyping A General Regression Neural Network with SAS

Last time when I read the paper “A General Regression Neural Network” by Donald Specht, it was exactly 10 years ago when I was in the graduate school. After reading again this week, I decided to code it out with SAS macros and make this excellent idea available for the SAS community. The prototype of

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Draw nicer Classification and Regression Trees with the rpart.plot package

June 19, 2013
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Draw nicer Classification and Regression Trees with the rpart.plot package

by Joseph Rickert The basic way to plot a classification or regression tree built with R’s rpart() function is just to call plot. However, in general, the results just aren’t pretty. As it turns out, for some time now there has been a better way to plot rpart() trees: the prp() function in Stephen Milborrow’s rpart.plot package. This function...

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Model Selection in Bayesian Linear Regression

June 17, 2013
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Model Selection in Bayesian Linear Regression

Previously I wrote about performing polynomial regression and also about calculating marginal likelihoods. The data in the former and the calculations of the latter will be used here to exemplify model selection. Consider data generated by and suppose we wish to fit a polynomial of degree 3 to the data. There are then 4 regression The post Model...

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General Regression Neural Network with R

June 16, 2013
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General Regression Neural Network with R

Similar to the back propagation neural network, the general regression neural network (GRNN) is also a good tool for the function approximation in the modeling toolbox. Proposed by Specht in 1991, GRNN has advantages of instant training and easy tuning. A GRNN would be formed instantly with just a 1-pass training with the development data.

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Robust logistic regression

June 7, 2013
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Corey Yanofsky writes: In your work, you’ve robustificated logistic regression by having the logit function saturate at, e.g., 0.01 and 0.99, instead of 0 and 1. Do you have any thoughts on a sensible setting for the saturation values? My intuition suggests that it has something to do with proportion of outliers expected in the The post Robust...

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Using R: drawing several regression lines with ggplot2

June 2, 2013
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Using R: drawing several regression lines with ggplot2

Occasionally I find myself wanting to draw several regression lines on the same plot, and of course ggplot2 has convenient facilities for this. As usual, don’t expect anything profound from this post, just a quick tip! There are several reasons we might end up with a table of  regression coefficients connecting two variables in different

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How logistic regression work ?

May 31, 2013
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How logistic regression work ?

Discussing with a non statistician colleague, it seems that the logistic regression is not intuitive; Some basics questions like : - Why don't use the linear model? - What's logistic function? - How can we compute by hand, step by step t...

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Regression regularization example

May 31, 2013
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Regression regularization example

Recently I needed a simple example showing when application of regularization in regression is worthwhile. Here is the code I came up with (along with basic application of parallelization of code execution). Assume you have 60 observations and 50 expla...

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