Thank you very much, Petr. I define my model based on same experiment
data. The trend of (A,B) and the relationship with ev are assumed as
1/A = a + b*ev + c*ev^2
1/B = d + e*exp(f*ev)
Both (1/A) and (1/B) are supposed to decrease (nonlinearly) with
increasing ev. The initial values I used are computed based on above
equations. I knew that the data is quite noisy. My purpose is to use
the model such that when given an interested ev, a curve of (N,CSR)
can be built.
Is it possible to force R to find (a,b,c,d,e,f,g) based on my
equations? Or, is there any other way I can do to improve the model?
Thank you very much for your help.
BTW, when I ran your codes in R, I got several errors like
"Error in sum(rr[-(1:npar)]^2) : subscript out of bounds"
"Error in nls(formula = formula, data = data, start = start, control
= control) :
singular gradient "
"Error in nls(formula = formula, data = data, start = start, control
= control) :
number of iterations exceeded maximum of 50 "
"Error in nlsModel(formula, mf, start, wts) :
singular gradient matrix at initial parameter estimates "
But the plot is OK. I don't if these errors matters. Thanks again for
your response. Hope someone can help me to find a solution to my
problem.
Niner
----------------------------------------------------
Niner, Seattle
[EMAIL PROTECTED]
On Oct 1, 2007, at 1:33 AM, Petr PIKAL wrote:
> Althoug I am not an expert in nonlinear regression, it seems to me
> that
> your data are quite noisy. I presume you have got your model from
> elsewhere and now you try to use it for your data. Looking at it
> and using
> nlme I suppose you will not get such definite model based on your
> data.
>
> Try
>
>> data1$ev <- ordered(data1$ev)
>> data1.gr <- groupedData(N~CSR|ev, data1)
>> out <- nlsList(N~CSR/(A+B*CSR), data1.gr, start= c(A=1, B=1))
>> out1<- nlme(out)
>> ranef(out1)
> A B
> 0.2 8.667265e-05 -6.267700e-04
> 0.3 -3.139092e-04 2.206547e-03
> 0.4 4.044455e-06 -2.349364e-05
> <snip>
>
> 3.6 -7.402470e-08 -5.312590e-06
> 5 -3.844779e-06 2.942158e-05
>
>> plot(as.numeric(row.names(ranef(out1))), ranef(out1)[,1])
>> plot(as.numeric(row.names(ranef(out1))), ranef(out1)[,2])
>
> You can clearly see that A and B random efect values are oscilating
> around
> 0 with increasing "ev" which seems to me that A and B are
> independent on
> ev
>
> Regards
> Petr
> [EMAIL PROTECTED]
>
> [EMAIL PROTECTED] napsal dne 01.10.2007 02:37:52:
>
>> Hi, I am new to R. I don't have strong background of statistics. I am
>> a student of Geotechnical Engineering. I tried to run a nonlinear
>> regression for a three-variable function, that is
>>
>> N = f(CSR, ev) # N is a function of CSR and ev, and N = CSR/(A
>> +B*CSR), wherer (A,B) are function of ev.
>>
>> N, CSR and ev are observed in the experiments.
>> Following is my R script.
>>
>> rm(list=ls())
>> library(nlme)
>>
>> # assign data
>> N <- c
>> (30.03,16.62,10.88,36.47,20.24,38.17,36.47,34.80,19.00,32.37,14.40,35
>> .63
>
>> ,
>> 19.00,17.79,33.98,31.58,31.58,35.63,20.24,31.58,29.27,22.18,27.77,25.
>> 60,
>
>> 19.00,7.05,34.80,29.27,29.27,17.79,10.42,31.58,17.79,17.20,11.36,19.0
>> 0,2
>
>> 9.27,12.33,22.18,22.18,14.40,31.58,19.00,9.52,33.17,13.87,19.00,21.52
>> ,11
>
>> .36,22.84,9.96,6.68,20.88,9.96,11.84,20.24,19.61,17.20,17.20)
>>
>> CSR <- c
>> (0.25,0.42,0.12,0.438,0.49,0.42,0.47,0.46,0.24,0.45,0.37,0.46,0.337,0
>> .36
>
>> ,
>> 0.334,0.346,0.399,0.44,0.246,0.33,0.413,0.23,0.45,0.45,0.44,0.106,0.3
>> 33,
>
>> 0.256,0.345,0.44,0.153,0.348,0.23,0.25,0.122,0.183,0.201,0.128,0.23,0
>> .24
>
>> ,
>> 0.129,0.438,0.228,0.111,0.409,0.14,0.24,0.20,0.22,0.22,0.152,0.094,0.
>> 131
>
>> ,0.123,0.155,0.28,0.204,0.149,0.193)
>>
>> ev <- c
>> (0.2,0.3,0.4,0.5,0.5,0.5,0.6,0.6,0.8,1,1.0,1.0,1.1,1.1,1.2,1.2,1.2,1.
>> 2,1
>
>> .
>> 3,1.3,1.3,1.3,1.3,1.3,1.4,1.5,1.5,1.5,1.5,1.5,1.6,1.6,1.6,1.6,1.7,1.7
>> ,1.
>
>> 7,1.7,1.7,1.7,1.8,1.8,1.9,1.9,1.9,1.9,1.9,2.0,2.1,2.1,2.3,2.4,2.4,2.7
>> ,2.
>
>> 8,3.3,3.4,3.6,5.0)
>>
>> # set the data frame
>> data1<- data.frame(cbind(N,CSR,ev))
>> # the initial values of parameters
>> para1.st <- c(a=75.4,b=165.9,c=-22.4,d=0,e=0.8,f=-0.28,g=0)
>> para2.st <- c(a=31.3,b=-176.9,c=53.41,d=0,e=75.2,f=-0.18,g=0)
>> # call nls funciton
>> try.control <- c(maxiter=100, minFactor=1/4096)
>> out <- nls(N~CSR/(1/(a+b*ev+c*(ev^2))+CSR/(d+e*exp(f*ev)))-g, data1,
>> start=para1.st, control=try.control, trace=T)
>>
>>
>> The data is from experiments and the pattern of data is scatter quite
>> a bit. I want to find the best fit coefficients (a,b,c,d,e,f,g) for
>> the data.
>> I have two different sets of initial values for try. But in both
>> cases I got error message of
>> "singular gradint"
>>
>> What can I do for this error? Is there any other nonlinear regression
>> model I can try? This problem is kind of emergency. I really hope
>> someone can help me out. Any comment is appreciated. Thanks a lot.
>>
>>
>>
>> Niner
>> -----------------------------------------------
>> Yi-Min Huang
>> Civil & Environmental Engineering
>> U. of Washington
>> 206-6165697
>>
>>
>> ----------------------------------------------------
>> Niner, Seattle
>> [EMAIL PROTECTED]
>>
>>
>>
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
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> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-
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