[R] Odp: nls convergence trouble

Wed Sep 3 17:57:48 CEST 2008

Hi

Excel fit is not exceptionally good. Try

 fff<-function(a,b) (V + b * m * a + C0 * V * b - ((C0 * V * b)^2 + 2 * C0 
*
+     b * V^2 - 2 * C0 * V * m * a * b^2 + V^2 + 2 * V * m * a *
+     b + (b * m * a)^2)^(1/2))/(2 * b * m)

and with attached data frame

plot(Qe,fff(364,0.0126))
abline(0,1)

you clearly see linear relationship in smaller values but quite chaotic 
behaviour in bigger ones (or big deviation of experimental points from 
your model).

So it is up to you if you want any fit (like from Excel) or only a good 
one (like from R). 

Seems to me that simple linear could be quite a good choice although there 
is some nelinearity.

fit<-lm(Qe~Ce+C0+V+m)
summary(fit)

Call:
lm(formula = Qe ~ Ce + C0 + V + m)

Residuals:
    Min      1Q  Median      3Q     Max 
-16.654  -8.653   2.426   9.971  11.912 

Coefficients:
              Estimate Std. Error t value Pr(>|t|) 
(Intercept) -8.148e+02  1.330e+03  -0.613 0.549254 
Ce          -6.894e-02  4.982e-03 -13.839 6.02e-10 ***
C0           3.284e-02  1.676e-03  19.589 4.26e-12 ***
V            2.153e+06  4.607e+05   4.674 0.000300 ***
m           -4.272e+04  1.218e+04  -3.509 0.003167 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 10.87 on 15 degrees of freedom
Multiple R-squared: 0.9903,     Adjusted R-squared: 0.9877 
F-statistic: 381.3 on 4 and 15 DF,  p-value: 6.91e-15 

plot(predict(fit), Qe)
abline(0,1)

Regards
Petr

r-help-bounces at r-project.org napsal dne 03.09.2008 16:01:36:

> Hi,
> 
> Parameters assessment in R with nls doesn't work, though it works fine 
with
> MS Excel with the internal solver :(
> 
> 
> I use nls in R to determine two parameters (a,b) from experimental data. 

> 
>         m         V        C0         Ce        Qe
> 1  0.0911 0.0021740  3987.581   27.11637  94.51206
> 2  0.0911 0.0021740  3987.581   27.41915  94.50484
> 3  0.0911 0.0021740  3987.581   27.89362  94.49352
> 4  0.0906 0.0021740  5981.370   82.98477 189.37739
> 5  0.0906 0.0021740  5981.370   84.46435 189.34188
> 6  0.0906 0.0021740  5981.370   85.33213 189.32106
> 7  0.0911 0.0021740  7975.161  192.54276 233.30310
> 8  0.0911 0.0021740  7975.161  196.52891 233.20797
> 9  0.0911 0.0021740  7975.161  203.07467 233.05176
> 10 0.0906 0.0021872  9968.951  357.49157 328.29824
> 11 0.0906 0.0021872  9968.951  368.47609 328.03306
> 12 0.0906 0.0021872  9968.951  379.18904 327.77444
> 13 0.0904 0.0021740 13956.532 1382.61955 350.33391
> 14 0.0904 0.0021740 13956.532 1389.64915 350.16485
> 15 0.0904 0.0021740 13956.532 1411.87726 349.63030
> 16 0.0902 0.0021740 15950.322 2592.90486 367.38460
> 17 0.0902 0.0021740 15950.322 2606.34599 367.06064
> 18 0.0902 0.0021740 15950.322 2639.54301 366.26053
> 19 0.0906 0.0021872 17835.817 3894.12224 336.57036
> 20 0.0906 0.0021872 17835.817 3950.35273 335.21289
> 21 0.0906 0.0021872 17835.817 3972.29367 334.68320
> 
> the model "LgmAltformula" is
> 
> Qe ~ (V + b * m * a + C0 * V * b - ((C0 * V * b)^2 + 2 * C0 * 
>     b * V^2 - 2 * C0 * V * m * a * b^2 + V^2 + 2 * V * m * a * 
>     b + (b * m * a)^2)^(1/2))/(2 * b * m)
> 
> the command in R is
> 
> 
> 
nls(formula=LgmAltFormula,data=bois.DATA,start=list(a=300,b=0.01),trace=TRUE
> ,control=nls.control(minFactor=0.000000009))
> 
> R has difficulties to converge and stops after the maximum of iterations
> 
> 64650.47 :  2.945876e+02 3.837609e+08 
> 64650.45 :  2.945876e+02 4.022722e+09 
> 64650.45 :  2.945876e+02 1.695669e+09 
> 64650.45 :  2.945876e+02 5.103971e+08 
> 64650.44 :  2.945876e+02 8.497431e+08 
> 64650.41 :  2.945876e+02 1.515243e+09 
> 64650.36 :  2.945877e+02 5.482744e+09 
> 64650.36 :  2.945877e+02 2.152294e+09 
> 64650.36 :  2.945877e+02 7.953167e+08 
> 64650.35 :  2.945877e+02 7.625555e+07 
> Erreur dans nls(formula = LgmAltFormula, data = bois.DATA, start = 
list(a =
> 300,  : 
>   le nombre d'itérations a dépassé le maximum de 50
> 
> 
> The parameters "a" and "b" are estimated to be 364 and 0.0126 with Excel
> with the same data set.
> I tried with the algorithm="port" with under and upper limits. One of 
the
> parameter reaches the limit and the regression stops.
> 
> How can I succeed with R to make this regression? 
> 
> 
> Regards/Cordialement
> 
> -------------
> Benoit Boulinguiez
> Ph.D
> Ecole de Chimie de Rennes (ENSCR) Bureau 1.20
> Equipe CIP UMR CNRS 6226 "Sciences Chimiques de Rennes"
> Campus de Beaulieu, 263 Avenue du Général Leclerc
> 35700 Rennes, France
> Tel 33 (0)2 23 23 80 83
> Fax 33 (0)2 23 23 81 20
> http://www.ensc-rennes.fr/ 
> 
> 
> 
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