Hello, I'm working on some fairly standard regression models (linear, logistic, and poisson.) Unfortunately, the data is rather messy.
A visual inspection, using either a histogram or a density plot indicates some significant outliers. Furthermore, summary statistics of the data indicate the same thing. If I fit a linear regression in R using the "lm" command, I can then plot the model to look at residuals, etc. I'm interesting in re-fitting the model with a N% of the high leverage points removed. (Large data set, want to fit "most" of the data.) Is there a computational way to get the leverage for each data point? That way I can subset the data skipping N% of the highest leverage ones. Thanks! -- Noah Silverman, M.S., C.Phil UCLA Department of Statistics 8117 Math Sciences Building Los Angeles, CA 90095 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
