Check out the drc package. Also, please read the last line to every message to r-help regarding reproducible code and note that function names are case sensitive in R so NLS is not the same as nls.
On Dec 21, 2007 7:36 PM, <[EMAIL PROTECTED]> wrote: > I am writing a program for automated (i.e. no user intervention - for > biologists) iteratively reweighted least-square fit to a function of the form > "reading ~ exp(lm2)/(1 + (dose/exp(lm3))^exp(lm4)" using case weight > proportional to the mean, e.g., E(reading). Because for some datasets the > solution is sensitive to starting values, I first use OPTIM() with > Nelder-Mead to locate the solution, and then plug the solution into NLS() > (default algorithm) using the appropriate weights as a way to retrieve SEs, > deviance, etc rather than computing these from OPTIM results. > > The problem I have discovered is the following. OPTIM() arrives at the > correct solution that minimizes the objective function, and I have confirmed > this in numerous examples in Excel. When this solution is plugged into > NLS(), the first evaluation yields the same value of the objective function > as OPTIM(). NLS() then attempts steps, and apparently finds a slightly > different solution with a smaller value for the objective function. However, > this new solution actually does not give a smaller objective function value, > but rather a larger one, when verifed against an independent computation. > Hence, it appears that NLS is getting confused and taking a step based on > erroneous computation of the objective function. > > An example is below, with an abridged OPTIM output followed by NLS output: > > -- ************** START OUTPUT ************************** > -- [1] "Drug = Melphelan" > -- [1] "Cell line = CHLA-266" > -- Nelder-Mead direct search function minimizer > -- function value for initial parameters = 3.052411 > -- Scaled convergence tolerance is 3.05241e-12 > -- Stepsize computed as 1.801225 > -- BUILD 4 288782670697.238953 3.052411 > -- LO-REDUCTION 6 40.478286 3.052411 > -- HI-REDUCTION 8 27.015234 3.052411 > -- . > -- . > -- . > -- LO-REDUCTION 208 0.763370 0.763370 > -- LO-REDUCTION 210 0.763370 0.763370 > -- Exiting from Nelder Mead minimizer > -- 212 function evaluations used > -- m2 m3 m4 > -- 1 80846342 1.190286e-05 1.049291 > > [***NOTE - the correct solution parameters values are the line above on the > absolute scale, which are the values below on the ln scale, representing the > first evaluation by NLS. This gives the correct objective function value of > 0.763370, and hence agrees with OPTIM at this point ***] > > -- 0.7633697 : 18.20806090 -11.33873192 0.04811485 > -- It. 1, fac= 1, eval (no.,total): ( 1, 1): new dev = 0.7504 > -- 0.7504 : 18.19909405 -11.34554803 0.05560241 > -- It. 2, fac= 1, eval (no.,total): ( 1, 2): new dev = 0.750387 > -- 0.7503866 : 18.19933189 -11.34796180 0.05429375 > -- It. 3, fac= 1, eval (no.,total): ( 1, 3): new dev = 0.750387 > -- . > -- . multiple step halving attempts > -- . > -- It. 14, fac= 3.8147e-06, eval (no.,total): ( 4, 39): new dev = 0.750387 > -- It. 14, fac= 1.90735e-06, eval (no.,total): ( 5, 40): new dev = 0.750387 > -- > -- Formula: reading ~ exp(lm2)/(1 + (dose/exp(lm3))^exp(lm4)) > -- > -- Parameters: > -- Estimate Std. Error t value Pr(>|t|) > -- lm2 18.19934 0.02084 873.252 <2e-16 *** > -- lm3 -11.34795 0.08316 -136.459 <2e-16 *** > -- lm4 0.05435 0.04103 1.325 0.191 > -- --- > -- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > -- > -- Residual standard error: 0.1147 on 57 degrees of freedom > -- > -- Number of iterations till stop: 13 > -- Achieved convergence tolerance: 2.611e-08 > -- Reason stopped: step factor 9.53674e-007 reduced below 'minFactor' of > 1e-006 > -- > -- Warning message: > -- In nls(reading ~ exp(lm2)/(1 + (dose/exp(lm3))^exp(lm4)), start = list(lm2 > = > -- log(beta_nm$m2), : > -- step factor 9.53674e-07 reduced below 'minFactor' of 1e-06 > -- > -- *** END OUTPUT ********************************************************* > > Note the objective function value of .750387 at parameter values 18.19934, > -11.34795, 0.05435. However, objective function value at this solution is > actually 0.776426587, by Excel computation. > > Can anyone suggest what I might be doing wrong, or is this a bug or anomaly > of the NLS algorithm? > > Thanks in advance > > Richard Sposto (Los Angeles). > > > > -- > This message was sent on behalf of [EMAIL PROTECTED] at openSubscriber.com > http://www.opensubscriber.com/message/[EMAIL PROTECTED]/7530379.html > > ______________________________________________ > 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.
