I am interested in a statistical comparison of multiple (5) time series' generated from modeling software (Hydrologic Simulation Program Fortran). The model output simulates daily bacteria concentration in a stream. The multiple time series' are a result of varying our representation of the stream within the model.
Our main question is: Do the different methods used to represent a stream produce different results at a statistically significant level? We want to compare each otput time series to determine if there is a difference before looking into the cause within the model. In a previous study, the Kolmogorov-Smirnov k-sample test was used to compare multiple time series'. I am unsure about the strength of the Kolmogorov-Smirnov test and I have set out to determine if there are any other tests to compare multiple time series'. I know htat R has the ks.test but I am unsure how this test handles multiple comparisons. Is there something similar to a pairwise.t.test with a bonferroni corection, only with time series data? Does R currently (v 2.3.0) have a comparison test that takes into account the strong serial correlation of time series data? Kyle Hall Graduate Research Assistant Biological Systems Engineering Virginia Tech ______________________________________________ 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