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From |
Maarten buis <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Testing for Trend Linearity |

Date |
Fri, 5 Sep 2008 09:18:41 +0100 (BST) |

--- [email protected] wrote: > I ran (in stata 9) > xi: xtpois injury age, i(id) > xi: xtpois injury age i.ageg, i(id) > where "injury" is a binary variable, "age" is continuous and "ageg" > is "age" as a categorical variable. All the p-values (age, _Iageg_2, > and _Iageg_3) were >0.05. > Does this imply the trend does not deviate from linearity (at the > 0.05 level)? No, for two reasons: 1) _Iageg_2 and _Iageg_3 represent a deviation from the linear trend within their interval. So the hypothesis would be _Iageg_2 and _Iagage_3 are both simultaneously equal to zero. The way to test such an hypothesis is to use -test- or convenient shortcuts exist when using -testparm-. 2) This is not a test of non-linearity, but a test of one particular and peculiar type of non-linearity: you are creating a sort of step function with sloping steps. The example gives shows graphs of the linear and non-linear trend. If you think population averaged models are ok for your situation than -fracpoly- and -mvrs- are very nice alternatives. -fracpoly- is part of official Stata, so more on that can be found by typing -help fracpoly-. -mvrs- is described in (Royston and Sauerbrei 2007) The example also shows a problem with using -poisson-, and -xtpoisson- for binary data: There is no way to restrict the predicted probabilities to be less than 1. For that reason using -logit- and -xtlogit- is preferable, especially if you have continuous covariates. Hope this helps, Maarten *------------- begin example ------------------ sysuse auto, clear poisson foreign mpg predict ir_lin egen mpg_g = cut(mpg), group(3) xi: poisson foreign mpg i.mpg_g testparm _Impg_g* predict ir_nonlin twoway line ir_* mpg, sort *---------------- end example ----------------- (For more on how to use examples I sent to the Statalist, see http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html ) Patrick Royston and Willi Sauerbrei (2007) Multivariable modeling with cubic regression splines: A principled approach. The Stata Journal 7(1): 45--70. http://www.stata-journal.com/article.html?article=st0120 ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * 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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