# 1662 search results for "regression"

## Scale back or transform back multiple linear regression coefficients: Arbitrary case with ridge regression

April 10, 2015
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SummaryThe common case in data science or machine learning applications, different features or predictors manifest them in different scales. This could bring difficulty in interpreting the resulting coefficients of linear regression, such as one featur...

## A Speed Comparison Between Flexible Linear Regression Alternatives in R

March 25, 2015
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Everybody loves speed comparisons! Is R faster than Python? Is dplyr faster than data.table? Is STAN faster than JAGS? It has been said that speed comparisons are utterly meaningless, and in general I agree, especially when you are comparing apples and oranges which is what I’m going to do here. I’m going to compare a couple of alternatives to...

## Regression Models, It’s Not Only About Interpretation

March 22, 2015
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$k$

Yesterday, I did upload a post where I tried to show that “standard” regression models where not performing bad. At least if you include splines (multivariate splines) to take into accound joint effects, and nonlinearities. So far, I do not discuss the possible high number of features (but with boostrap procedures, it is possible to assess something related to...

## SAS PROC MCMC example in R: Nonlinear Poisson Regression Multilevel Random-Effects Model

March 8, 2015
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I am slowly working my way through the PROC MCMCexamples. Regarding these data, the SAS manual says: 'This example uses the pump failure data of Gaver and O’Muircheartaigh (1987) to illustrate how to fit a multilevel random-effects model with PROC MCMC. The number of failures and the time of operation ...

## 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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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

## Inequalities and Quantile Regression

February 6, 2015
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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...

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

February 1, 2015
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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

## 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...

## 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