I am looking at differences in dates of maximum counts of geese at 3 sites
in the UK. I am testing to see if the date of maximum count is different
between 3 sites.

My data look like these created in R:

  df <- data.frame(day=c(sample(70:80, 10), sample(75:85, 10), sample(80:90,
10)),
                   year=rep(2000:2009, 3),
                   site=paste('site', sort(rep(1:3, 10))))

Head of dataframe:

  day year   site
1  78 2000 site 1
2  76 2001 site 1
3  71 2002 site 1
4  73 2003 site 1
5  75 2004 site 1
6  74 2005 site 1
  
The variable 'day' is the day number on which the maximum count of geese was
recorded in that year. So in rows 1, the the maximum count was recorded 78
days from the 1st Jan in that year.

I considered carrying out a simple ANOVA with day as response variable and
site as predictor variable. However I've become aware that this would
violate the assumption of independence. Is there a mixed effects model that
is able to handle the dependence of the data? Alternatively, would some sort
of time series analysis be more appropriate here?

Thanks
Ross



	[[alternative HTML version deleted]]

