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Notes:
      1.  (-m# option or -set memory-) 1.00 MB allocated to data
      2.  Command line editing disabled
      3.  Stata running in batch mode


running /home/bpp/bin/profile.do ...

. do ratkow2.do 

. /* NIST/ITL StRD
> Dataset Name:  Ratkowsky2        (Ratkowsky2.dat)
> 
> File Format:   ASCII
>                Starting Values   (lines 41 to 43)
>                Certified Values  (lines 41 to 48)
>                Data              (lines 61 to 69)
> 
> Procedure:     Nonlinear Least Squares Regression
> 
> Description:   This model and data are an example of fitting
>                sigmoidal growth curves taken from Ratkowsky (1983).
>                The response variable is pasture yield, and the
>                predictor variable is growing time.
> 
> 
> Reference:     Ratkowsky, D.A. (1983).  
>                Nonlinear Regression Modeling.
>                New York, NY:  Marcel Dekker, pp. 61 and 88.
> 
> 
> 
> 
> 
> Data:          1 Response  (y = pasture yield)
>                1 Predictor (x = growing time)
>                9 Observations
>                Higher Level of Difficulty
>                Observed Data
> 
> Model:         Exponential Class
>                3 Parameters (b1 to b3)
> 
>                y = b1 / (1+exp[b2-b3*x])  +  e
> 
> 
> 
>           Starting Values                  Certified Values
> 
>         Start 1     Start 2           Parameter     Standard Deviation
>   b1 =   100         75            7.2462237576E+01  1.7340283401E+00
>   b2 =     1          2.5          2.6180768402E+00  8.8295217536E-02
>   b3 =     0.1        0.07         6.7359200066E-02  3.4465663377E-03
> 
> Residual Sum of Squares:                    8.0565229338E+00
> Residual Standard Deviation:                1.1587725499E+00
> Degrees of Freedom:                                6
> Number of Observations:                            9 
> */
. 
. clear

. 
. scalar N         = 9 

. scalar df_r      = 6

. scalar df_m      = 3

. 
. scalar rss       = 8.0565229338E+00

. scalar rmse      = 1.1587725499E+00

. 
. scalar b1        = 7.2462237576E+01  

. scalar seb1      = 1.7340283401E+00

. scalar b2        = 2.6180768402E+00  

. scalar seb2      = 8.8295217536E-02

. scalar b3        = 6.7359200066E-02  

. scalar seb3      = 3.4465663377E-03

. 
. qui input double(y x)

. 
. nl ( y = {b1} / (1+exp({b2}-{b3}*x)) ), init(b1 100 b2 1 b3 0.1) eps(1e-10)
(obs = 9)

Iteration 0:  residual SS =  19915.85
Iteration 1:  residual SS =  6184.434
Iteration 2:  residual SS =  6178.702
Iteration 3:  residual SS =  6175.971
Iteration 4:  residual SS =  6167.185
Iteration 5:  residual SS =  6164.692
Iteration 6:  residual SS =  6142.645
Iteration 7:  residual SS =   6111.21
Iteration 8:  residual SS =  6028.496
Iteration 9:  residual SS =  5985.768
Iteration 10:  residual SS =   4696.54
Iteration 11:  residual SS =    1943.7
Iteration 12:  residual SS =  1210.456
Iteration 13:  residual SS =  10.19422
Iteration 14:  residual SS =  8.056614
Iteration 15:  residual SS =  8.056523
Iteration 16:  residual SS =  8.056523
Iteration 17:  residual SS =  8.056523
Iteration 18:  residual SS =  8.056523
Iteration 19:  residual SS =  8.056523

      Source |       SS       df       MS            Number of obs =         9
-------------+------------------------------         F(  3,     6) =   4521.89
       Model |  18215.3637     3  6071.78789         Prob > F      =    0.0000
    Residual |  8.05652293     6  1.34275382         R-squared     =    0.9996
-------------+------------------------------         Adj R-squared =    0.9993
       Total |  18223.4202     9  2024.82447         Root MSE      =  1.158773
                                                     Res. dev.     =  24.54421

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          b1 |   72.46224   1.734027    41.79   0.000     68.21923    76.70525
          b2 |   2.618077   .0882953    29.65   0.000     2.402026    2.834128
          b3 |   .0673592   .0034466    19.54   0.000     .0589258    .0757926
------------------------------------------------------------------------------
 (SEs, P values, CIs, and correlations are asymptotic approximations)

. 
. assert N    == e(N)

. assert df_r == e(df_r)

. assert df_m == e(df_m)

. 
. lrecomp _b[b1] b1 _b[b2] b2 _b[b3] b3 () /*
> */ _se[b1] seb1 _se[b2] seb2 _se[b3] seb3 () /*
> */ e(rmse) rmse e(rss) rss

_b[b1]               8.0
_b[b2]               7.8
_b[b3]               7.6
-------------------------
min                  7.6

_se[b1]              6.2
_se[b2]              6.0
_se[b3]              6.3
-------------------------
min                  6.0

e(rmse)             10.4
e(rss)              11.8

. 
. 
end of do-file