ruchita gupta <ruchitarg123 <at> gmail.com> writes:
>
> Hello
>
> I was trying to estimate the weibull model using nls after putting OLS
> values as the initial inputs to NLS.
> I tried multiple times but still i m getting the same error of Error in
> nlsModel(formula, mf, start, wts) :
> singular gradient matrix at initial parameter estimates.
>
> The Program is as below
>
> > vel <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14)
> > df <- data.frame(conc, vel)
> > df
> conc vel
> 1 0.077 1
> 2 0.328 2
> 3 0.882 3
> 4 1.195 4
> 5 1.884 5
> 6 3.577 6
> 7 6.549 7
> 8 13.000 8
> 9 33.690 9
> 10 52.220 10
> 11 90.140 11
> 12 166.050 12
> 13 233.620 13
> 14 346.890 14
> > plot(df$vel, df$conc)
> > para0.st <- c(K=450,
> + alpha=0.054,beta=3.398 )
> > fit0 <- nls(
> + conc~ K-(K*exp(-(vel/alpha)^beta)), df, start= para0.st,trace=T)
> Error in nlsModel(formula, mf, start, wts) :
> singular gradient matrix at initial parameter estimates
>
> I will be highly thankful if some one can please let me know where is the
> mistake as i m unable to trace it.
>
> Thanks
> Ruchita
>
This is a better fit:
> nls(conc~K+alpha*vel^beta,df,start<-c(K=-0.7,alpha=0.0003,beta=5.3),trace=T)
349.1687 : -0.7000 0.0003 5.3000
264.4474 : -0.7507993362 0.0002808334 5.3172851705
263.6331 : -0.7559514801 0.0002816438 5.3169303333
263.6331 : -0.7558506044 0.0002816331 5.3169456595
Nonlinear regression model
model: conc ~ K + alpha * vel^beta
data: df
K alpha beta
-0.7558506 0.0002816 5.3169457
residual sum-of-squares: 263.6
Number of iterations to convergence: 3
Achieved convergence tolerance: 1.599e-06
>
----------------------------------------------------------------
Erwin Kalvelagen
Amsterdam Optimization Modeling Group
er...@amsterdamoptimization.com
http://amsterdamoptimization.com
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