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From |
jl591164@albany.edu |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: interprating orthogonal polynomial regression |

Date |
Thu, 22 Jul 2010 16:33:26 -0400 (EDT) |

Hi All, 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? 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. 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. Does this mean i need to use age as the time variable instead? However, the longitudinal data is collected at each "time" point, but not at each age. Just to check how age may influence the trend of y, I ran a multilevel logistic regression with the three orthogonal polynomials of time and the three orthogonal polynomials of age. The are all significant. So what should be my final model of the trend of Y, just use the polynomials of time, or also the polynomials of age? Thanks a lot. Junqing * * 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/

**Follow-Ups**:**Re: st: interprating orthogonal polynomial regression***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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