Influence.ME: don’t specify the intercept

June 18, 2009
By

(This article was first published on Curving Normality » R-Project, and kindly contributed to R-bloggers)

Just recently, I was contacted by a researcher who wanted to use influence.ME to obtain model estimates from which iteratively some data was deleted. In his case, observations were nested within an area, but there were very unequal numbers of observations in each area. Unfortunately, he wasn’t able to use ...

To leave a comment for the author, please follow the link and comment on his blog: Curving Normality » R-Project.
R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series,ecdf, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Tags: , , ,

Comments are closed.

Top 7 articles of the week

  1. R is the easiest language to speak badly
  2. Great Maps with ggplot2
  3. Select operations on R data frames
  4. The anatomy of a Twitter conversation, visualized with R
  5. Measuring associations between non-numeric variables
  6. googleVis 0.2.14 is released
  7. Monty Hall by simulation in R

Sponsors

Mango solutions
mango-solutions.com
Zero Inflated Models and Generalized Linear Mixed Models with R Zero Inflated Models and Generalized Linear Mixed Models with R. Zuur, Saveliev, Ieno (2012).