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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
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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
---------------------------------------------------------------