I have mean-variance plots of annual NDVI from different data sets. I would like to compare the trajectories of these plots, but not sure what would be the best way to do it. Any suggestions are welcome.
Compare the slopes?
Something like this maybe?
http://r-eco-evo.blogspot.co.uk
Thank you for the suggestions however my problem may be more complex. To give a better idea of what I mean I attached an example of two plots that I would like to compare. These plots probably take the form of directional graphs with the nodes being the mean and variance of NDVI in a specific year and the vertices between the nodes are dictated by the time series. I would like to know how similar these two graphs are but I am not sure if the stats intended for comparison of directed graphs would be valid in this case.
Look at my paper:
https://www.researchgate.net/publication/230996023_The_application_of_statistical_verification_in_studies_in_fish_variability?ev=prf_pub . May give You some idea on this problem.
Article The application of statistical verification in studies in fi...
Hallo Yolandi,
There are many ways of doing this. It would be fun working through them to see what they revealed !
One is to calculate the euclidian distance between the pair of points for each year. That is, take the point for, say, 2005 in plot a and that for 2005 in plot b. Then calculate the distance beween these - by basic trigonometry. If the trajectories were identical then you'd expect the distances to all be zero. In practice they'd be a distribution of values around a mean. You can then test whether the mean obtained is significantly different from zero. (I would do this by repeatedly randomizing the plot b values against a stationary column of the a values and each time calculate the distances and their mean.)
Another possibility is to calculate similarity values between all pairs of points (that is beween the point for year 2005 in plot a and that for the same year in plot b). You'd use the mean and variance values for each year as the basic data. (I would suggest that you might also include kurtosis and skew of the distributions of NDVI values. All these are essentially multivariate measures of the characteristics of the NDVI values for each year in the two localities.
Once you have the similarity values you can try techniques such as clustering to see whether the values for plot a cluster separately from those for plot b. If there's no separation the trajectories are the same.
There's many more possibilities ! best wishes, Andrew
Thank you Andrew. Very good idea. Looks like I'm about to embark on an experimental stats journey;-)
Perhaps a circular statistics analysis can be useful in this case. Read more in: Circular Statistics in Biology, by Edward Batschelet, Academic Press, 1981. A resume of the techniques can be found in Zar, 1999.
It worth reading up on anyway. But start with Fisher NI 1995 Statistical Analysis of Circular Data. Cambridge University Press.
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