The actual calculation for each element in the vector I am looking to
produce is simply the reference point minus the position. Interesting
that you suggest it might be possible using -reshape-. I am not sure
if I can go down this route as the next step, once I have produced the
vector is to multiply it by its transpose and a diagonal vector of
dimensional saliencies (this is part of the formula for calculating
weighted euclidean distances over multiple dimensions). I have not
used Mata before so I think I better start familiarising myself with
it!
Thanks,
James
2009/9/9 Austin Nichols <[email protected]>:
> James Cross<[email protected]> :
> This would be fairly easy to program in Mata, but you can also
> -reshape- to wide format and calculate the distances using -generate-
> if you know how you are going to treat missing values. Once you can
> specify the actual calculations for your example, it will be easier to
> specify a method.
>
> On Wed, Sep 9, 2009 at 9:12 AM, James Cross<[email protected]> wrote:
>> Hi all,
>>
>> I have a large panel dataset which contains information on different
>> actor positions on different issues (dimensions) within different
>> legislative proposals. Each actor position has a saliency score
>> associated with the position by which I hope to weight the importance
>> of the issue/dimension to that actor. In essence, I am trying to
>> calculate the weighted Euclidean distances between each actors'
>> position and a reference point.
>>
>> In order to do this I first need to create submatrices of the dataset,
>> structured as row vectors, that contain the distances between the two
>> points of interest for each actor for each issue/dimension for each
>> proposal. That is, I should end up with a row vector for each actor of
>> distances between the actors' position and the reference point. The
>> number of elements in this row vector is determined by the number of
>> issues in each proposal in the panel data. While I can do this for
>> each observation individually, I am wondering if it is possible to get
>> stata to do it automatically to save me the effort.
>>
>> The resulting vector will then need to be multiplied by its transpose
>> and a diagonal matrix of issue salience for each actor but that cannot
>> be done until I have created the individual actor distance vectors.
>> There is also an issue with missing data in that sometimes the
>> reference point will be missing and some actors will not have
>> positions on all of the issues/dimensions.
>>
>> The data is structured as follows:
>>
>>
>> proposal issue actor position ref point
>> 04163 1 1 0 0
>> 04163 2 1 0 0
>> 04163 1 2 0 0
>> 04163 2 2 0 0
>> 00032 1 1 100 0
>> 00032 2 1 50 n/a
>> 00032 3 1 100 100
>> 00032 4 1 100 0
>> 00032 1 2 100 0
>> 00032 2 2 0 n/a
>> 00032 3 2 0 100
>> 00032 4 2 0 0
>> 00032 1 3 40 0
>> 00032 2 3 100 n/a
>> 00032 3 3 100 100
>> 00032 4 3 100 0
>>
>> The resulting vector would look like this for proposal 00032 actor 1:
>> [100 n/a 0 100], and for proposal 04163 actor 1: [0 0].
>>
>> I am not sure if this is even possible in stata or if it is, how much
>> programming is involved.
>>
>> Any suggestions welcome.
>> Thanks in advance.
>>
>> James
>>
>
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>
--
James Cross
Department of Political Science
Trinity College
2-3 College Green
Dublin 2
Republic of Ireland
Mobile: +353 (0) 85 74 88 330
Email: [email protected]
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