st: RE: Question on a quasi-time series

[Nick Cox <n.j.cox@durham.ac.uk>]

I wouldn't tackle it that way at all. Ranking values has nothing to do with time or dependence structure! Time series problems are rarely such that you can just tweak a critical level! 

You have a time series, just one with lots of missing data. You can still -tsset- on daily date and look at e.g. the autocorrelation function. You can still look for trends. 

The bigger question is why you have gaps. If the gappiness is capricious as far as the phenomena are concerned, that is the best news. On the other hand, it seems far more likely that either the organisms or the observers were visible or active in the field at certain times, e.g. seasonality for the organisms or it was research time with good weather and light and absence of teaching or committee work for the observers. 

(I am interpolating here a memory that your data are essentially ecological; I may be misremembering, or this problem may be different. Either way, just abstracting a problem from its context often obscures it and makes good advice more difficult.) 

Nick 
n.j.cox@durham.ac.uk 

rachel grant

I have daily count data over a number of years totalling 317 cases.
However it's not a true time series because I have not got a full year
for each year, just a month or two.
The data are likely to be partially serially dependent within years
but not between years. So I am not sure how to correct for the
possible serial dependency.
What I have tried is ranking the data 1-317 and then using this
ranking to use the command "tsset". Will this work? Alternatively
should I simply increase my confidence level to p= 0.01 and not bother
trying to correct for the autocorrelation? Thanks!

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