Announcement

Collapse
No announcement yet.

Re: Directional Statistics

Collapse
This topic is closed.
X
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • Re: Directional Statistics

    I'll continue on with Patrick Sparto's response to Gulshan Khanna's request
    about circular statistics. I've used circular (and in 3-D, spherical)
    statistics to analyze directional tuning of muscle activity. Another good text
    (for multi-dimensional data) is Statistical Analysis of Spherical Data, by
    Fisher, Lewis and Embleton (Cambridge University Press, 1987). I found the
    books by Fisher et al. and Batschelet to be very helpful for developing
    practical methods for data analysis, while those by Mardia were much more
    theoretical.

    I believe these methods are very useful for biomechanical analysis, though I
    agree with Patrick, that they may be limited because they are not as
    well-developed as linear statistics. With some work, it should be possible to
    develop circular or spherical analogues of repeated measures or mixed factorial
    design. I have developed Matlab functions for many of the basic directional
    statistics and would happy to share them.

    Anita Vasavada

    Patrick Sparto wrote:

    > I would like to respond to Dr. Ghanna's previous question regarding the best
    > way to analyze circadian rhythms. I believe the best method is using
    > directional (aka circular)statistics, which is an area or statistics
    > concerned with data that is arranged around a circle, such as a compass or
    > clock. I have been reading two such texts:
    >
    > Circular Statistics in Biology - Batschelet, 1981, Academic Press
    > Directional Statistics, Mardia and Jupp, 2000, Wiley
    >
    > The former is more didactic and the second more theoretical.
    >
    > I have three further questions associated with this topic.
    >
    > 1) Commonly in the biomechanics literature, statistical comparison of joint
    > angles (i.e. circular data) is performed using conventional linear
    > statistics such as ANOVA. Has anyone examined whether the assumption of
    > linear normal distribution is appropriate in this case. I assume that a
    > circular normal (i.e. Von Mises) distribution would be better, but how much
    > of a difference is there? I know that ANOVA can be fairly robust with
    > non-(linear)normally distributed data. One limiting factor that I can see
    > with using the circular inferential stats is that the statistical models do
    > not seem to be as well developed as the general linear models. Consequently,
    > I have not seen in these textbooks ways to deal with repeated measures, or
    > mixed factorial (crossed and nested) designs.
    >
    > 2) Has anyone developed their own Matlab toolbox of directional statistics
    > functions that they would be willing to share. If not, I may have just
    > volunteered myself.
    >
    > 3) Does anyone know of any Statistics list servers where I could cross list
    > this posting, in order to get responses from the experts.
    >
    > Patrick Sparto, Ph.D., PT
    > University of Pittsburgh
    > Department of Physical Therapy
    > and Otolaryngology
    > psparto@pitt.edu
    >
    > ---------------------------------------------------------------
    > To unsubscribe send SIGNOFF BIOMCH-L to LISTSERV@nic.surfnet.nl
    > For information and archives: http://isb.ri.ccf.org/biomch-l
    > ---------------------------------------------------------------

    --
    Anita Vasavada, Ph.D.
    Assistant Professor
    Departments of Biological Systems Engineering and VCAPP
    Washington State University
    Pullman, WA 99164-6120
    voice: (509) 335-7533
    fax: (509) 335-2722
    vasavada@wsu.edu
    http://www.vetmed.wsu.edu/research_vcapp/vasavada.html

    ---------------------------------------------------------------
    To unsubscribe send SIGNOFF BIOMCH-L to LISTSERV@nic.surfnet.nl
    For information and archives: http://isb.ri.ccf.org/biomch-l
    ---------------------------------------------------------------
Working...
X