Hi, I agree with you that Excel is not the best tool for fittings, that's why I try to handle R.
But I need to use this specific model ("LgmAltFormula") and not a polynomial expression with the different parameters even if your method produced correct fitting. The parameters "a" and "b" are the Langmuir parameters that describe the adsorption of a compound onto activated carbon. I need to assess these parameters. Regards/Cordialement Benoit Boulinguiez -----Message d'origine----- De : Petr PIKAL [mailto:[EMAIL PROTECTED] Envoyé : mercredi 3 septembre 2008 17:58 À : Benoit Boulinguiez Cc : r-help@r-project.org Objet : Odp: [R] nls convergence trouble 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 [EMAIL PROTECTED] 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/ > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.