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Is a mixed effects appropriate?
2 messages · Ross Ahmed, Ben Bolker
Ross Ahmed <rossahmed at ...> writes:
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?
I would say that anova(lme(day~site, random=~1|year,data=df)) would be a reasonable model, allowing for random variation among years. I do think it would make sense to look for trends across time anova(lme(day~site*year, random=~1|year,data=df)) (there is a fixed effect and a random effect of year in this model, but it should be OK because the random effect treats year as a categorical variable ... as long as the fixed effect is a numeric (continuous) variable) You should definitely make sure to look at graphical representations of the data! But obviously your made-up data set doesn't have anything to see in it ...