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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: spline regression |

Date |
Fri, 7 Mar 2008 22:19:47 +0000 (GMT) |

--- Mohammed El Faramawi <melfaram@yahoo.com> wrote: > 1) When should we use it? If you add a continous variable without spline terms, square terms, etc. to a model you think that it has a linear effect on the mean of your dependent variable (-regress-), the logit of success (-logit-), the log rate (-poisson-, -nbreg-, -zip-), the log hazard rate (-streg- or -cox-). If you think that that is not true, than you can use a spline. > 2)What conditions should be fulfilled before running > this model. e.g. should the Dependant variable be > categorical or linear, what about the independent > variable You can add a spline to whatever model, so your dependent variable, can be continous, bounded, discrete, categorical, or whatever else you can or cannot think of. A spline is added because you think that the effect of a variable is non-linear, so it makes only sense when the explanatory variable is continuous. > 3) How to interpret the results obtained from it. If you use a linear spline you can interpret the result of the first spline term as the effect of the variable before the first knot, the second spline term as the effect between the first and the second knot, etc. So in the example below: a year extra tenure leads to .22 dollars extra income if the tenure is less than 10 years, and .11 dollars extra income if tenure is more than 10 years. This means there is quite a hard break in the change in effect at the knot. You can let the effect change more gradually by specifying the -cubic- option (if you have Stata 10). The interpretation can now best be done using graphs. The graph below shows the predicted income for a white college graduate in a union job with an average age. *--------------------- begin example ------------------------ sysuse nlsw88, clear gen ln_w = ln(wage) gen black = race == 2 if race < . mkspline ten1 10 ten2 = tenure reg ln_w ten1 ten2 union age black collgrad mkspline tens=tenure, cubic nk(3) reg ln_w tens* union age black collgrad preserve sum age if e(sample) replace age = r(mean) replace union = 1 replace black = 0 replace collgrad = 1 predict yhat bys tenure: gen byte mark = _n== 1 twoway line yhat1 yhat2 tenure if mark, sort restore *-------------------- 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 ) Hope this helps, Maarten ----------------------------------------- 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/ ----------------------------------------- ___________________________________________________________ Rise to the challenge for Sport Relief with Yahoo! For Good http://uk.promotions.yahoo.com/forgood/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: spline regression***From:*David Airey <david.airey@vanderbilt.edu>

**References**:**st: spline regression***From:*Mohammed El Faramawi <melfaram@yahoo.com>

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