I've been doing a simple time-series analysis looking at the relationship between daily pneumonia hospitalizations and daily temperature. To mimic some of the literature, I've been including a time-trend to try to account for normal cyclical trends in hospitalization. So I've been using a function that looks something like this:
gam(pneucount ~ temp_f + s(day,bs="cr",k=(4*totalyears)+1), day being the enumerated day in the analysis (1-365 for a 1 year analysis). This seems to work well enough. What troubles me is when I think about doing an analysis focusing on winter days using more than one year of data. If I just delete the summer days from the dataset, the time trend spline is trying to anneal counts from the end of one winter with the beginning of another, which doesn't seem right to me. What's the route to a statistically defensible result? Is it as simple as using the subset option? Or would I need to create indicator variables for each winter I'm interested and work in a by statement somehow (with an extra term for the levels of that indicator, I assume)? Thanks in advance for helping a Epi student who's being exposed to all this for the first time. Sincerely, Kevin Sorensen ____________________________________________________________________________________ Park yourself in front of a world of choices in alternative vehicles. Visit the Yahoo! Auto Green Center. ______________________________________________ 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.