I have the following dataset with daily home range sizes (meter95, meter50) per individual:
trackId Date rain temp windSp distance flights age sex meter95 meter50
<fct> <date> <dbl> <dbl> <dbl> <int> <int> <fct> <fct> <int> <int>
1 AP002 2017-12-12 0 15.2 2.88 2311 5 adult male 123 10
2 AP002 2017-12-13 0.06 13.5 3.11 4289 9 adult male 50 8
3 AP002 2017-12-14 0.23 13.6 2.73 4722 11 adult male 111 4
4 AP002 2017-12-15 0.39 13.2 1.33 9297 28 adult male 164 110
5 AP002 2017-12-16 0.02 12.8 1.28 7848 20 adult male 155 29
6 AP002 2017-12-17 0.01 14.1 1.78 7252 16 adult male 198 91
I am trying to figure out which distribution to fit to the home range data. However, the data seems to be very right-skewed:
It does not include many 0's (only 10/356), like discussed in these posts: Probability distribution for heavy zero, right skewed data Fitting a heavy right skewed distribution
I tried to fit other regular distributions, through fitdistr()
but none of these fit (just showing a few here):
I also drew a Cullen and Frey graph to see which distribution fits best (as suggested here: How to determine which distribution fits my data best?), and it seems to be a bèta distribution:
I am quite new to this, so I am not sure how to go from here and whether the Cullen and Frey graph really gives me the right distribution. I read on other forums that it doesn't always give the best fit. I also thought my data could maybe fit an inverse-Gaussian distribution, for example, but Cullen and Frey does not include that option.
Also, I wonder if it might be possible to transform my data so it does fit one of the more common distributions? Is that possible when building glmer()
models?
meter50
andmeter95
? Are zero values for these even meaningful, or is this a code for "missing"? $\endgroup$ – Stephan Kolassa Aug 4 '18 at 20:57