Hello I am currently investing software code metrics for a variety of software projects of a company to determine the worst parts of software products according to specified quality characteristics. As the gathering of metrics correlates with effort, I would like to find a subset of the metrics preserving significant predictive power for the "problem value" while using the least amount of code metrics.
I have the results of 25 metrics for 6 software projects for a combined 9355 "individuals", i.e. software parts with metrics. However, as many metrics only measure metric values above a predefined limit, 58% of the responses for independent variables are 0. Which method can I use to determine a reduced set of independent variables with significant predictive power? As I do not have a statistics background, I would also appreciate a simple explanation of the chosen method and sensible choices for parameters, so that I will be able to infer the reduced set of software metrics to keep. Thank you in advance! Johannes -- View this message in context: http://n4.nabble.com/Method-for-reduction-of-independent-variables-tp1013171p1013171.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
