-------- Original Message --------
Subject:        Re: Analyzing ontogenetic trajectory angles
Date:   Wed, 20 Jul 2011 15:32:55 -0400
From:   David Katz <[email protected]>
To:     [email protected]



Dennis,

Thanks for your response.  And for the group.  The responses I've gotten
have been remarkable.

David

On Wed, Jul 20, 2011 at 10:49 AM, morphmet
<[email protected]
<mailto:[email protected]>> wrote:



    -------- Original Message --------
    Subject: Re: Analyzing ontogenetic trajectory angles
    Date: Wed, 20 Jul 2011 13:48:46 -0400
    From: Dennis E. Slice <[email protected] <mailto:[email protected]>>
    To: [email protected] <mailto:[email protected]>

    I'll just comment on a few technical points I haven't see addressed in
    the replies. See below...


    On 7/19/11 1:28 PM, morphmet wrote:



        -------- Original Message --------
        Subject: Analyzing ontogenetic trajectory angles
        Date: Mon, 18 Jul 2011 14:46:19 -0400
        From: David Katz <[email protected] <mailto:[email protected]>>
        To: [email protected] <mailto:[email protected]>



        Hello,

        I have read several morphometrics papers which test for significant
        differences in ontogenetic trajectory between two groups (species,
        subspecies, etc) by calculating the "angle" between their growth
        trajectories. However, parts of (or even lots of) the method remain
        unclear to me.

        First, it seems that calculation of the angle requires
        calculation of
        two simple regression lines, one for each group, with the angle
        being
        the arc subtended by the two lines. One axis for these regression
        plots/calculations is the distribution of specimens along a PC
        which is
        significantly correlated with size or age (usually the first
        PC). But
        it is not clear to me what the second axis is. Log centroid size?


    You don't need to explicitly specify two axes with which to work. A
    multivariate regression of a bunch of variables on, say, age or size
    will give you coefficients for each variable. These form a vector
    pointing in some direction in multivariate space. Do the same for
    another group, and you have another direction. There is an angle formed
    by any two directional vectors in a space and the cosine of this angle
    can be computed using the dot product of the vectors.



        Second, the significance of the angle is tested by randomly
        permuting
        specimens between the two groups 1000 or more times, then
        calculating a
        new angle for each permutation. Significance is then tested
        against the
        distribution of permutation-generated angles. But what is the
        permutation procedure? Do we only permute a single specimen each
        time?


    If you are looking for differences between groups, what you permute is
    group membership. If your data are organized such that the first m are
    group A and the next n are group B, then you simply randomize the order
of the data vector and keep taking the first m as group A and the next n
    as group B. You can also create a group membership vector and shuffle
    that. Or in other cases, you might want to resample within groups to
    assess the variability of the result (whatever it is) for that group.


        Third, if I'm understanding the method correctly, then absent an
        additional scaling step, the two lines from which the angle is
        calculated are not likely to be of the same length. How is this
        accounted for, if at all?


    Angles are invariant to size. The dot product operation I mentioned
    above includes a standardization for size. For vectors a and b, the
    formula is:

    cos(theta) = (a dot b)/sqrt((a dot a)(b dot b))

    or directly

    theta = arccos((a dot b)/sqrt((a dot a)(b dot b)))

    The sqrt((a dot a)(b dot b)) part is the size standardization. The dot
    product for two vectors is sum_i (a_i times b_i) for the elements of a
    and b.


        Any help, or a reference to a good, explicit journal or book
        explanation, would be very much appreciated.


Most multivariate texts have sufficient chapters or appendices on matrix
    algebra and vector geometry for this. Manly, B. F. J. "Randomization,
    Bootstrap and Monte Carlo Methods in Biology" provides an accessible
    discussion.

    -ds


        Thanks.

        David Katz
        Doctoral Candidate
        Department of Anthropology--Evolutionary Wing
        University of California, Davis
        Young Hall 204

        /--Trying to focus on one distraction at a time/--



    --
    Dennis E. Slice
    Associate Professor
    Dept. of Scientific Computing
    Florida State University
    Dirac Science Library
    Tallahassee, FL 32306-4120
            -
    Guest Professor
    Department of Anthropology
    University of Vienna
            -
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    ==============================__==========================





--
David Katz
Doctoral Candidate
Department of Anthropology--Evolutionary Wing
University of California, Davis
Young Hall 204

/--Trying to focus on one distraction at a time/--

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