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I have many (n=317,823) observations on two variables. I want to fit a bivariate distribution to my observations, in order to identify descriptive features of the distribution (quantiles). However, my data do not appear normal or log-normal and I haven't found a package on the relevant CRAN task view that can help me. I am hoping to learn:

  • if there is an existing workflow to fit a somewhat idiosyncratic pdf like the one below
  • whether I am 'asking the wrong question' given my weak math skills. Maybe there is an easier way to approach my problem.

Context: my data originate from satellite observations of forest harvest around the world. For a randomly selected subset of these observations (1 pixel/100km2) I have sampled two global raster layers, one showing forest canopy height and one showing time to access cities. I believe that these are two observable aspects (height, accessibility) of a multidimensional distribution of 'forest quality', which contains many other unbelievable aspects (e.g., species composition). I am trying to characterize the joint distribution so that I may categorize subsequent observations of forest harvest as falling within particular (joint) quantiles.

Sampling data plotted in base via table() and persp(): enter image description here

Data are strictly positive by construction...and that's a funny looking (asymmetrical) pdf. Clearly the long tail is important. I used the excellent r package {fitdistrplus} to look at each variable separately, following the vignette.

Some diagnostic results for dimension 'height': enter image description here

...and for dimension 'accessibility to cities': enter image description here

The first looks like it could be described by a log-normal or gamma, and the second by gamma...possible a bivariate gamma distribution could fit the joint density well?

Current approach (my best option) is to accept the (large) inaccuracy and model the joint distribution as log-normal, e.g. by taking logs of both variables and fitting with package fMultivar, and then attempting to work out isolines as described in this post.

here are two downsampled versions of the dataset, obtained as data[sample(nrow(data),round(nrow(data)/scale)),], with scale=10 ("medium") or 100 ("small")

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  • $\begingroup$ here is a related-but-not-useful post in which OP was told to take a different approach $\endgroup$
    – antifrax
    Commented Mar 26, 2019 at 12:59
  • $\begingroup$ There's the mixsmsn package for fitting skew normal, Student $t$, skew $t$ etc. distributions. Might be worth a try. $\endgroup$
    – corey979
    Commented Mar 26, 2019 at 17:12
  • $\begingroup$ My approach would be to re-sample down to a 50x50 grid (2500 total data points) and perform an equation search, then fit all of the data to that equation. I would down-sample as an equation search with 300,000 data points would be too much for the equipment I personally have available, and a 50x50 re-sample grid visually looks like it would be quite sufficient for a good equation search based on your plots. $\endgroup$
    – James Phillips
    Commented Mar 26, 2019 at 20:06
  • $\begingroup$ @corey979, those distributions are defined over negative values: my values (height, travel time) are strictly positive (0s were dropped as bad data). I've dialed down the bin width on the accessibility histogram to show that distribution better. I don't think the smallest bin is important (can explain) so that pretty much looks like decreasing exponential to me. $\endgroup$
    – antifrax
    Commented Mar 26, 2019 at 22:59
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    $\begingroup$ My open source curve and surface fitting web site, zunzun.com, has hundreds of known, named equations and a "function finder" to perform equation searches using them. Post a link to the downsampled data and I will see if it might suggest any candidate surface equations of the form "z = f(x,y)". It's worth a try to see what turns up. $\endgroup$
    – James Phillips
    Commented Mar 27, 2019 at 0:41

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