Dear R-ecology subscribers,



Here is a small contamination data set: ‘chlord’ extracted from a publication (Bahner and Oglesby USEPA):

   V1       V2

1   0 0.000000

2   7 1.579784

3   9 1.857332

4  14 1.944483

5  21 2.079181

6  28 1.939519

7  30 2.000000

8  35 2.000000

9  42 1.924279

10 49 1.740363

11 56 1.361728



I would like to translate this old SAS script (provided), to actual R code :

PROC SORT; BY SPECIES CONC TISSUE;

PROC NLIN BEST = 0.5 ITER = 200 METHOD = MARQUARDT; BY 

SPECIES CONC TISSUE;

PARMS         A = 0.1 TO 0.3 by 0.1

                        C = 0.1 TO 0.7 by 0.2

                        D = ─0.1 TO 0.3 by 0.2;

Q = (A + 10**(─C*DAY))); E = 28;

R = (A + 10**EXP(─D*(DAY─E)));

MODEL Y = Q**(─1) ─ R**(─1);

DER.A = ─ (Q** (─2)) + R** (─1);

DER.C = (Q** (─2))* (10** (─C*DAY))*DAY*LOG(10);

DER.D = ─10* (DAY ─ E)*EXP (─D*(DAY ─ E))*R**(─2);

_____WEIGHT____ = DAY;

 

I tried the nls function:

fm<-nls(V2~(A+(10^(-C*V1)))^-1-(A+10^exp(-D*(V1-28)))^-1, data=chlord, trace=TRUE, start=c(A=0.1,C=0.1,D=-0.1), nls.control (maxiter = 200))

with an extensive research through web, but found no information about how to take into account the DER.A, DER.C and DER.D in R code, for optimization. 

 

thank you in advance for any information

Dominique

 

______________________________

D. Monti

Marine Lab. 

University of Antilles and Guyane

Little Island of Guadeloupe, FWI





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