Re: st: Collapsing data to daily data

[Brigham Whitman <brighamj@gmail.com>]

Yes, at first I wasn't going to collapse my data for a survival
analysis.  It is a huge data set (68 individual white-tailed deer with
a total of 72,000 data points with up to 4 data points per day per
deer), but Stata and R could handle it fine.  But my advisors for my
thesis said I should collapse the data, and another man I contacted
who has done the same analysis suggested I should collapse it, so I
did.  That man had mentioned something about how I might inadvertently
be increasing the amount of time each animal is at risk if I use
multiple locations per animal per day.  I can't say that I totally
understand why that is.  It does make more sense to me now to look at
daily data, and I've incorporated a 'Cumulative days of snow depth
over 38 cm' variable and running averages for certain variables over
14 and 28 day moving windows, which I am not sure how I would do if I
hadn't collapsed the data.  But yes, I should have a better idea of
why I would "coarsen" my data and lose some of that accuracy.  Thank
you for the input.

-Brigham Whitman



On Tue, Mar 1, 2011 at 9:44 AM, Nick Cox <n.j.cox@durham.ac.uk> wrote:
> Usual spelling is Cox. (No, not me, not even a relative.)
>
> More importantly, I don't see that you need to coarsen your data for a survival analysis -- unless the sheer size of the dataset is problematic.
>
> It could even be that time of day data has a secondary bearing on survival...
>
> Nick
> n.j.cox@durham.ac.uk
>
> Brigham Whitman
>
> Yes, these options worked out for me, thank you.
>
> I am collapsing the data set to prepare it for a cox proportional
> hazard model.  Each data point already has a few variables (distance
> to cover, distance traveled, snow depth encountered) with measurements
> for each point that I can average together for each day before I make
> the model.
>
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>

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