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Dear Jonathan,<br>
<br>
Thank you for your swift reply! VI sounds great - I had encountered
BrailleR, but I'd missed this. It seems amazing, thank you!<br>
<br>
It doesn't provide one convenient number, but it does enable blind
people to explore their data in a similar way to 'sighted' (?)
people. This is great!<br>
<br>
Maybe I'll have time to write a method for scatterplots, e.g.
generating an X by X grid and describing the number of datapoints in
each grid cell in a table form? That would be faster without doing
it from a scatterplot, I guess. Anyway, if I get around to it, I'll
send it around to the list!<br>
<br>
The sonification option also seems very interesting! Apparently,
packages exist to let R produce sounds . . . This seems promising:
<a class="moz-txt-link-freetext" href="http://www.inside-r.org/packages/cran/sound/docs/play">http://www.inside-r.org/packages/cran/sound/docs/play</a> - maybe also
worth exploring later on :-)<br>
<br>
Again, thank you very much, kind regards,<br>
<br>
Gjalt-Jorn<br>
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<br>
Behavior change research | Health psychology <br>
Intervention development | Applied social psychology </span>
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<div class="moz-cite-prefix">On 2015-07-30 9:57, Godfrey, Jonathan
via BlindRUG wrote:<br>
</div>
<blockquote
cite="mid:D59DA89C3CD73C44A799E7087F8E6A9E1898E322@tur-exch-node1.massey.ac.nz"
type="cite">
<pre wrap="">Hi,
This is a great question.
Personally, I would be hoping the sample size is sufficient to allow me to increase the number of bins in a histogram to convince myself that the presence of bimodality is real. I can get the information from the VI() command on the histogram creation command. For example:
VI(hist(x))
I'm still trying to find a way of conveying a density curve to a blind audience. It's very easy to follow a tactile representation of a density, but conversion to text is proving challenging. I suspect this is one situation where sonification of the curve might also prove to be an answer.
Cheers,
Jonathan
-----Original Message-----
From: BlindRUG [<a class="moz-txt-link-freetext" href="mailto:blindrug-bounces@nfbnet.org">mailto:blindrug-bounces@nfbnet.org</a>] On Behalf Of Gjalt-Jorn Peters via BlindRUG
Sent: Thursday, 30 July 2015 6:29 p.m.
To: <a class="moz-txt-link-abbreviated" href="mailto:BlindRUG@nfbnet.org">BlindRUG@nfbnet.org</a>
Cc: Gjalt-Jorn Peters
Subject: [BlindRUG] Measures of modality: understanding distribution shapes
Dear BlindRUG readers,
I'm working on revising our first statistics course (in a psychology curriculum). We're updating it such that R can be used, to make it accessible to blind students.
I'm currently trying to figure out how to let blind students assess distribution 'shapes'. They can compute skewness and kurtosis, but it's also necessary to assess modality (how many 'mountains' there are). Is any of you familiar with any measures for this?
How do you normally assess the 'shape' of a distribution?
Thank you very much in advance, kind regards,
Gjalt-Jorn Peters
Dutch Open University
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More information and useful links about using R as a blind person can be obtained at:
<a class="moz-txt-link-freetext" href="http://R-Resources.massey.ac.nz">http://R-Resources.massey.ac.nz</a>
Look for help using R commands by reading the accessible e-book "Let's Use R Now" compiled by Jonathan Godfrey at:
<a class="moz-txt-link-freetext" href="http://R-Resources.massey.ac.nz/lurn/front.html">http://R-Resources.massey.ac.nz/lurn/front.html</a>
</pre>
</blockquote>
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