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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 advice. 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 >> instead? > > 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 > -------------------------- > > > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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