I'm attempting to analyse a large chunk of empiric measurements subject to two parametrised transformations. In essence, the functions take 3 'count' parameters - and returns a sequence of floats in each configuration. I'm expecting (hoping) to see some interesting patterns emerge when appropriate parameters are selected. I anticipate that the patterns might be relative between sequences returned for each function - and/or relate to patterns of some kind in the parameters. In case it's relevant, the 3 'count' parameters roughly correspond to:
- 'window size' on the underlying data over which summary statistics are calculated
- A number of consecutive windows used to compute a single summary statistic (i.e. the trade-off between greater spatial or greater temporal accuracy)
- A 'minimum age' - an offset into history of the underlying data.
The summary statistics are non-trivial but are independently sensitive to all three parameters.
I'm interested in visualisation techniques - suited to ad-hoc enquiry - that will help me experiment with this multi-dimensional data.
EDIT (In response to Peter Ellis): Your 'pun' is, essentially, my problem. I've not stored the data - instead, I can calculate it 'on the fly' from (otherwise opaque) bulk empirical data. If I were to store the results of my calculation in matrix form, it would be a 4D matrix... The first three dimensions are all temporal - as identified above - then the index for the resulting sequence is also temporal - and the values are a dimensionless problem-specific scalar metric... represented using a floating point number. One might imagine the data to visualise as associating a distribution (function) with each 'voxel' in a cuboid. I visualise these sequences (representing the distribution associated with each voxel) in isolation, as regularly sampled continuous functions. One way I've imagined the data is with the sequences as line-graphs, and three 'tweaking knobs' I can use to 'tune in' a result... another is as any one of thee surface plots with two 'tweaking knobs'. A more elaborate visualisation animates a range of values for one of the 'tweaking knobs' - leaving me one parameter to set manually. The structure of the data does not suggest which parameters are best in which context - and the answer to this may depend upon both the empiric data (yet to be collected in full) and the scale of the parameters. A significant complication is that I don't - as yet - know how sensitive the results of my calculations will be to the parameters above... One objective is to find values for the first two parameters to minimise the impact of the third. Beyond that, and analogies between my parameters and time, I'm afraid the data is pretty abstract... there's no real-world object from which to draw inspiration.