[R] When is *interactive* data visualization useful to use?

[Rainer M Krug]

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On 02/11/2011 08:21 PM, Claudia Beleites wrote:
> Dear Tal, dear list,
> 
> I think the importance of interactive graphics has a lot do with how
> visual your scientific discipline works. I'm spectroscopist, and I think
> we are very visually oriented: if I think of a spectrum I mentally see a
> graph.
> 
> So for that kind of work, I need a lot of interaction (type: plot,
> change a bit, plot again), e.g.
> One example is the removal of spikes from Raman spectra (caused e.g. by
> cosmic rays hitting the detector). It is fairly easy to compute a list
> of suspicious signals. It is already much more complicated to find the
> actual beginning and end of the spike. And it is really difficult not to
> have false positives by some automatic procedure, because the spectra
> can look very different for different samples. It would just take me far
> longer to find a computational description of what is a spike than
> interactively accepting/rejecting the automatically marked suspicions.
> Even though it feels like slave work ;-)
> 
> Roughly the same applies for the choice of pre-processing like baseline
> correction. A number of different physical causes can produce different
> kinds of baselines, and usually you don't know which process contributes
> to what extent. In practice, experience suggests a method, I apply it
> and look whether the result looks as expected. I'm not aware of any
> performance measure that would indicate success here.
> 
> The next point where interaction is needed pops up as my data has e.g.
> spatial and spectral dimensions. So do the models usually: e.g. in a
> PCA, the loadings would usually capture the spectroscopic direction,
> whereas the scores belong to the spatial domain. So I have "connected"
> graphs: the spatial distribution (intensity map, score map, etc.), and
> the spectra (or loadings).
> As soon as I have such connections I wish for interactive visualization:
> I go back and forth between the plots: what is the spectrum that belongs
> to this region of the map? Where on the sample are high intensities of
> this band? What is the substance behind that: if it is x, the
> intensities at that other spectral band should correlate. And then I
> want to compare this to the scatterplot (pairs plot of the PCA score) or
> to a dendrogram of HCA...
> 
> Also, exploration is not just prerequisite for models, but it frequently
> is already the very proper scientific work (particularly in basic
> science). The more so, if you include exploring the models: Now, which
> of the bands are actually used by my predictive models? Which samples do
> get their predictions because of which spectral feature?
> And, the "statistical outliers" may very well be just the interesting
> part of the sample. And the outlier statistics cannot interprete the
> data in terms of interesting ./. crap.
> 
> For presentation* of results, I personally think that most of the time a
> careful selection of static graphs is much better than live interaction.
> *The thing where you talk to an audience far awayf from your work
> computer. As opposed to sitting down with your client/colleague and
> analysing the data together.
> 
>> It could be argued that the interactive part is good for exploring (For
>> example) a different behavior of different groups/clusters in the
>> data. But
>> when (in practice) I approached such situation, what I tended to do
>> was to
>> run the relevant statistical procedures (and post-hoc tests)
> As long as the relevant measure exists, sure.
> Yet as a non-statistician, my work is focused on the physical/chemical
> interpretation. Summary statistics are one set of tools for me, and
> interactive visualisation is another set of tools (overlapping though).
> 
> I may want to subtract the influence of the overall unchanging sample
> matrix (that would be the minimal intensity for each wavelength). But
> the minimum spectrum is too noisy. So I use a quantile. Which one?
> Depends on the data. I'll have a look at a series (say, the 2nd to 10th
> percentile) and decide trading off noise and whether any new signals
> appear. I honestly think there's nothing gained if I sit down and try to
> write a function scoring the similarity to the minimum spectrum and the
> noise level: the more so as it just shifts the need for a decision (How
> much noise outweighs what intensity of real signal being subtracted?).
> It is a decision I need to take. With number or with eye. And after all,
> my professional training was thought to enable me taking this decision,
> and I'm paid (also) for being able to take this decision efficiently
> (i.e. making a reasonably good choice within not too long time).
> 
> After all, it may also have to do with a complaint a colleague from a
> computational data analysis group once had. He said the bad thing with
> us spectroscopists is that our problems are either so easy that there's
> no fun in solving them, or they are too hard to solve.
> 
>> - and what I
>> found to be significant I would then plot with colors clearly dividing
>> the
>> data to the relevant groups. From what I've seen, this is a safer
>> approach
>> then "wondering around" the data (which could easily lead to data
>> dredging
>> (were the scope of the multiple comparison needed for correction is
>> not even
>> clear).
> Sure, yet:
> - Isn't that what validation was invented for (I mean with a proper,
> new, [double] blind test set after you decided your parameters)?
> - Summarizing a whole data set into a few numbers, without having looked
> at the data itself may not be safe, either:
> - The few comparisons shouldn't come at the cost of risking a bad
> modeling modelling strategy and fitting parameters because the data was
> not properly examined.
> 
> My 2 ct,
> 
> Claudia (who in practice warns far more frequently of multiple
> comparisons and validation sets being compromised (not independent) than
> of too few data exploration ;-) )

These are very interesting and valid points. But which tools are
recommended / usefull for interactive graphs for data evaluation? I
somehow have difficulties getting my head around ggobi, and haven't yet
tried out mondian (but I will). Are there any other ones (as we are ion
the R list - which integrate with R) which can be recommended?

Rainer

> 


- -- 
Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation
Biology, UCT), Dipl. Phys. (Germany)

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