r-project

R Tutorial Series: Introduction to The R Project for Statistical Computing (Part 1)

October 11, 2009 | John M. Quick

R is a free, cross-platform, open-source statistical analysis language and program. It is also an alternative to expensive commercial statistics software such as SPSS. The environment for R differs from the typical point and click interface found in mo... [Read more...]

Delete rows from R data frame

October 8, 2009 | heuristicandrew

Deleting rows from a data frame in R is easy by combining simple operations. Let’s say you are working with the built-in data set airquality and need to remove rows where the ozona is NA (also called null, blank or missing). The method is a conce... [Read more...]

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

April 29, 2009 | Rense Nieuwenhuis

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...]

useR! 2009 acceptance: presenting influence.ME

April 23, 2009 | Rense Nieuwenhuis

The organizing committee of the useR! 2009 conference just informed me, that my submission for presenting my extension package influence.ME, has been accepted! Influence.ME is a new R package that I’m currently developing, with the indispensable ... [Read more...]

R-Sessions 32: Forward.lmer: Basic stepwise function for mixed effects in R

February 13, 2009 | Rense Nieuwenhuis

Intended to be a customized solution, it may have grown to be a little more. forward.lmer is an early installment of a full stepwise function for mixed effects regression models in R-Project. I may put in some work to extend it, or I may not. Neverthel... [Read more...]

R-Sessions 31: Combining lmer output in a single table (UPDATED)

February 5, 2009 | Rense Nieuwenhuis

There are various ways of getting your output from R to your publication draft. Most of them are highly efficient, but unfortunately I couldn’t find a function that combines the output from several (lmer) models and presents it in a single table.... [Read more...]

R-Sessions 30: Visualizing missing values

January 8, 2009 | Rense Nieuwenhuis

It always takes some time to get a grip on a new dataset, especially large ones. The code-books are often as indispensable as they are massive, and not always as clear as one would want. Routings, and resulting and strange patterns of missing values are at times difficult to find. ...