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RE: st: types and codes of the non-linear models


From   "BASSILI, Dr Amal STB/TDR" <bassilia@emro.who.int>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
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: statalist@hsphsun2.harvard.edu
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: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten Buis
Sent: Thursday, February 21, 2013 10:36 AM
To: statalist@hsphsun2.harvard.edu
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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