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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: types and codes of the non-linear models |

Date |
Thu, 21 Feb 2013 15:25:15 +0000 |

With a function like this, the parameters usually no longer have simple interpretations. There is an exception for -b3-. -b3- is in effect a kind of origin on the time axis for your model and it is estimated as about 2010. In this case, the very high t statistic and very low P-value are just a side-effect of the usual null hypothesis that the origin is "really zero", i.e. 0 AD. That hypothesis is not of scientific interest, or so I presume. This kind of modelling is usually lose-lose, unfortunately. One or two-parameter models miss important features of the data. More complicated models are more difficult to fit, or converge unsatisfactorily. Nick On Thu, Feb 21, 2013 at 2:00 PM, BASSILI, Dr Amal STB/TDR <bassilia@emro.who.int> wrote: > Further to my below mail, I have done the below non-linear regression to predict incidence of a disease over years and would like to know how to interpret the trend. Is it b1? And if b1= 1.6, does this mean that the average trend is 1.6% per year? > > > ----------------------------- > > Thanks, . predict incidence1_rate_hat > (option yhat assumed; fitted values) > > . twoway (fpfitci incidence1_rate_hat year) > > . nl log4 : incidence_rate year > (obs = 7) > > Iteration 0: residual SS = .1728985 > Iteration 1: residual SS = .1640116 > Iteration 2: residual SS = .1594486 > Iteration 3: residual SS = .1555352 > Iteration 4: residual SS = .1518877 > Iteration 5: residual SS = .1480812 > Iteration 6: residual SS = .1451413 > Iteration 7: residual SS = .1317394 > Iteration 8: residual SS = .1305299 > Iteration 9: residual SS = .1265793 > Iteration 10: residual SS = .1206557 > Iteration 11: residual SS = .1068023 > Iteration 12: residual SS = .1019377 > Iteration 13: residual SS = .0991159 > Iteration 14: residual SS = .0414042 > Iteration 15: residual SS = .0314478 > Iteration 16: residual SS = .0299035 > Iteration 17: residual SS = .0299035 > Iteration 18: residual SS = .0299035 > Iteration 19: residual SS = .0299035 > > Source | SS df MS > -------------+------------------------------ Number of obs = 7 > Model | .92438218 3 .308127393 R-squared = 0.9687 > Residual | .029903534 3 .009967845 Adj R-squared = 0.9373 > -------------+------------------------------ Root MSE = .0998391 > Total | .954285714 6 .159047619 Res. dev. = -18.32468 > > 4-parameter logistic function, incidence_rate = b0 + b1/(1 + exp(-b2*(year - b3))) > ------------------------------------------------------------------------------ > incidence_~e | Coef. Std. Err. t P>|t| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > /b0 | 2.656667 .0695158 38.22 0.000 2.435436 2.877897 > /b1 | 1.619345 1.376313 1.18 0.324 -2.760698 5.999388 > /b2 | 1.240626 .8727815 1.42 0.250 -1.536954 4.018206 > /b3 | 2010.526 1.460316 1376.77 0.000 2005.878 2015.173 > ------------------------------------------------------------------------------ > Parameter b0 taken as constant term in model & ANOVA table * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: types and codes of the non-linear models***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

**References**:**st: types and codes of the non-linear models***From:*"BASSILI, Dr Amal STB/TDR" <bassilia@emro.who.int>

**Re: st: types and codes of the non-linear models***From:*Maarten Buis <maartenlbuis@gmail.com>

**RE: st: types and codes of the non-linear models***From:*"BASSILI, Dr Amal STB/TDR" <bassilia@emro.who.int>

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