Dear fellow R users, I am struggling with the task of quantifying the statistical significance of changes in a discrete distribution over time. If I was to measure e.g. the age distribution of people entering a building on a daily basis, I would naturally observe fluctuations in that distribution. Clearly, small variations would be interpreted as "sampling noise" whereas major shifts would indicate sth. more substantial. How would I quantify this ? Would a ChiSquare test be an appropriate test for testing overall stationarity ? Or a two-way ANOVA decomposition ? Also, what if wanted to test specific days for significant deviation from my Null model instead of overall ? I am familiar with univariate time series change point detection algorithms but am not clear on how to translate these tools to the constrained/multivariate distribution setting.
Thanking you! Markus ______________________________________________ R-help@stat.math.ethz.ch 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.
