Posts Tagged ‘ regression ’

New edition of “R Companion to Applied Regression” – by John Fox and Sandy Weisberg

December 10, 2010
By
New edition of “R Companion to Applied Regression” – by John Fox and Sandy Weisberg

Just two hours ago, Professor John Fox has announced on the R-help mailing list of a new (second) edition to his book “An R and S Plus Companion to Applied Regression”, now title . “An R Companion to Applied Regression, Second Edition”. John Fox is (very) well known in the R community for many contributions to R, including the...

Read more »

Studying joint effects in a regression

October 7, 2010
By
Studying joint effects in a regression

We've seen in the previous post (here)  how important the *-cartesian product to model joint effected in the regression. Consider the case of two explanatory variates, one continuous (, the age of the driver) and one qualitative (, gasoline ve...

Read more »

Visualization of regression coefficients (in R)

July 2, 2010
By
Visualization of regression coefficients (in R)

Update (07.07.10): The function in this post has a more mature version in the “arm” package. See at the end of this post for more details. * * * * Imagine you want to give a presentation or report of your latest findings running some sort of regression analysis. How would you do it? This was exactly the question...

Read more »

Analysis of Covariance – Extending Simple Linear Regression

April 28, 2010
By
Analysis of Covariance – Extending Simple Linear Regression

The simple linear regression model considers the relationship between two variables and in many cases more information will be available that can be used to extend the model. For example, there might be a categorical variable (sometimes known as a covariate) that can be used to divide the data set to fit a separate linear

Read more »

Simple Linear Regression

April 23, 2010
By
Simple Linear Regression

One of the most frequent used techniques in statistics is linear regression where we investigate the potential relationship between a variable of interest (often called the response variable but there are many other names in use) and a set of one of more variables (known as the independent variables or some other term). Unsurprisingly there

Read more »

Social Media Analytics Research Toolkit (SMART@znmeb) Is Moving Into Private Beta

March 31, 2010
By

Download "Getting Started with the Social Media Analytics Research Toolkit" (pdf, 1.25 megabytes) Download the Social Media Analytics Research Toolkit My Social Media Analytics Research Toolkit is about to move into private beta. What's in the release?...

Read more »

In a nls star things might be different than the lm planet…

March 10, 2010
By

The nls() function has a well documented (and discussed) different behavior compared to the lm()’s. Specifically you can’t just put an indexed column from a data frame as an input or output of the model. > nls(data ~ c + expFct(data,beta), data = time.data, + start = start.list) Error in parse(text = x) : unexpected

Read more »

CRU graph yet again (with R)

December 13, 2009
By
CRU graph yet again (with R)

IowaHawk has a excellent article attempting to reproduce the infamous CRU climate graph using OpenOffice: Fables of the Reconstruction. We thought we would show how to produced similarly bad results using R. If the re-constructed technique is close to what was originally done then so many bad moves were taken that you can’t learn muchRelated posts:

Read more »

Introducing Influence.ME: Tools for detecting influential data in mixed models

April 29, 2009
By

I’m highly excited to announce that influence.ME is now available. Influence.ME is a new software package for R, providing statistical tools for detecting influential data in mixed models. It has been developed by Rense Nieuwenhuis, Ben Pelzer, a...

Read more »

R: Calculating all possible linear regression models for a given set of predictors

February 6, 2009
By
R: Calculating all possible linear regression models for a given set of predictors

Although the graphic at the left might not seem a 100% appropriate, it gives a hint to what I am about to do. I want to calculate all possible linear regression models with one dependent and several independent variables. I do not want to address bias and fitting issues or the question if this

Read more »