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RE: st: types and codes of the non-linear models
From
"BASSILI, Dr Amal STB/TDR" <[email protected]>
To
"[email protected]" <[email protected]>
Subject
RE: st: types and codes of the non-linear models
Date
Thu, 21 Feb 2013 14:00:35 +0000
Hi Maarten,
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
Amal
-----Original Message-----
From: BASSILI, Dr Amal STB/TDR
Sent: Thursday, February 21, 2013 4:00 PM
To: [email protected]
Subject: RE: st: types and codes of the non-linear models
Hi Maarten,
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. If b2 =0.87, does this mean that the average trend is 0.87% per year?
-----------------------------
nl exp2 : incidence_rate year
(obs = 7)
Iteration 0: residual SS = 66.15485
Iteration 1: residual SS = 66.15484
Source | SS df MS
-------------+------------------------------ Number of obs = 7
Model | 387.559441 0 . R-squared = 0.8542
Residual | 66.1548444 6 11.0258074 Adj R-squared = 0.8542
-------------+------------------------------ Root MSE = 3.320513
Total | 453.714286 6 75.6190476 Res. dev. = 35.58776
2-parameter exp. growth curve, incidence_rate = b1*b2^year
------------------------------------------------------------------------------
incidence_~e | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
/b1 | 3.9e+116 . . . . .
/b2 | .8763577 .0000164 53453.24 0.000 .8763176 .8763978
------------------------------------------------------------------------------
Parameter b1 taken as constant term in model & ANOVA table
Thanks,
Amal
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Maarten Buis
Sent: Thursday, February 21, 2013 10:36 AM
To: [email protected]
Subject: Re: st: types and codes of the non-linear models
On Wed, Feb 20, 2013 at 8:28 PM, BASSILI, Dr Amal STB/TDR wrote:
> Please let me know the types and STATA codes of the non-linear models that can forecast the incidence rate of a disease if linear regression cannot be used.
The number of options open to you is just too large to list here. We could write a book-length post here with lots of options and code.
However, this would require a lot of work from us (for free), and most of it would be useless to you as it would not apply to your problem.
In order to get a more useful response you need to narrow your question down by giving us more details on what you want to do.
-- Maarten
---------------------------------
Maarten L. Buis
WZB
Reichpietschufer 50
10785 Berlin
Germany
http://www.maartenbuis.nl
---------------------------------
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