1542 search results for "regression"

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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Bayesian model II regression

May 27, 2013
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Bayesian model II regression

Regression is a mainstay of ecological and evolutionary data analysis. For example, a disease ecologist may use body size (e.g. a weight from a scale with measurement error) to predict infection. Classical linear regression assumes no error in covariates; they are known exactly. This is rarely the case in ecology, and ignoring error in covariates can bias regression coefficient...

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When Does the Kinetic Theory of Gases Fail? Examining its Postulates with Assistance from Simple Linear Regression in R

When Does the Kinetic Theory of Gases Fail?  Examining its Postulates with Assistance from Simple Linear Regression in R

Introduction The Ideal Gas Law, , is a very simple yet useful relationship that describes the behaviours of many gases pretty well in many situations.  It is “Ideal” because it makes some assumptions about gas particles that make the math and the physics easy to work with; in fact, the simplicity that arises from these

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