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,
> 19.00,7.05,34.80,29.27,29.27,17.79,10.42,31.58,17.79,17.20,11.36,19.00,2
> 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
> ,
> 0.334,0.346,0.399,0.44,0.246,0.33,0.413,0.23,0.45,0.45,0.44,0.106,0.333,
> 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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