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st: multicolinearity test for multiple imputed longitudinal data

 From jl591164@albany.edu To statalist@hsphsun2.harvard.edu Subject st: multicolinearity test for multiple imputed longitudinal data Date Mon, 26 Jul 2010 15:36:57 -0400 (EDT)

Dear all,
My data has five waves and consists of five multiple imputed data sets. My
final model will be three-level random intercept logistic regression
model. I would like to test the multicolinearity of the independent
variables(most of them are dummy variables). What is a good way to get the
correlation matrix of the variables, e.g. by each wave? Or how to conduct
the multicolinearity test after fitting -mim, gllamm-model? Thanks for any
Junqing

> --- On Thu, 22/7/10, jl591164@albany.edu wrote:
>> I fitted a three level logistic regression of y on the
>> first, second, and third order of orthogonal polynomials
>> of time to examine the trend of y. Coefficients of the
>> three orthogonal polynomials are significant. The
>> signs of linear and cubic trend are negative and the
>> quadratic term is positive.
>>
>> I conclude that y has a cubic trend. The interpretation is
>> that as time increases, the probability of y first decrease.
>> With a further increase in time y appeared to increase. Then
>> at about 51 months(based on the graph of the sample mean of
>> y), y decreases again.
>>
>> What else should i interpret about the cubic trend? Do I
>> have to calculate the time points when the sings change?
>
> It is your argument, so you decide what you think is
> confincing or illuminating evidence and what is not. We
> can only make suggestions. Finding these points can be sorta
> nice, but they should not be taken too literaly, as they
> are to a large extend influenced by the functional form you
> assumed.
>
>> If so, i probably need to transform the coefficients of
>> orthogonal polynomials into coefficients for the original
>> time scale. I do not know how stata does this transform
>> after fitting a -mim:gllamm- model.
>
> *--------------- begin example ----------------
> sysuse auto, clear
> orthpoly weight, deg(3) generate(pw*)
> logit foreign mpg pw1-pw3 rep78
> orthpoly weight, deg(3) poly(P)
> matrix b = e(b)
> // extract the polynomials and the constant
> matrix b = b[1, "foreign:pw1".."foreign:pw3"], b[1,"foreign:_cons"]
> matrix b = b*P
> matlist b
>
> // check
> gen w1 = weight
> gen w2 = weight^2
> gen w3 = weight^3
>
> logit foreign mpg w1-w3 rep78
> *---------------- end example -----------------------
>
> Personaly, I like linear splines better, as they often provide
> a better balance between allowing for non-linear effects and
> giving directly interpretable coefficients. See -help mkspline-.
>
>> Then I need to think about why y has a cubic trend. One
>> possible explanation is age. With the increase in time, the
>> age of participants increase as well. The cubic trend may
>> because different age intervals have different trends.
>
> Assuming that participants aren't all born in the same year,
> you can add time and age, or time and year of birth, or age
> and year of birth, but not all three, as time - age = year of
> birth.
>
> There is a large literature on still trying to estimate
> these "age-period-cohort effects" which basically consists of
> proposing different constraints on one or more of these
> variables. Assuming that this constraint is true you can
> estimate all three, but you cannot test whether the constraint
> is true, so...
>
>> Does this mean i need to use age as the time variable
>
> There is only one person who can decide that, and that is you.
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
>
>
>
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