[R] Covariates in NLS (Multiple nonlinear regression)

[Bert Gunter]

I think you need to consult a local statistician, as you appear to be so far
out of your depth statistically that even the best-intentioned help from
this list may not suffice. For example, if I were that local statistician, I
would ask: what is the context? -- what is the underlying scientific issue
that led to this model? -- what are the data? -- etc. etc. before I would
even hazard advice on the statistical details(Please do not answer these
questions either publicly or privately, as they are just fyi's).

Bert Gunter
Genentech Nonclinical Biostatisics

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Lindsay Banin
Sent: Tuesday, August 25, 2009 9:02 AM
To: 'r-help at r-project.org'
Subject: [R] Covariates in NLS (Multiple nonlinear regression)

Dear R-users,

I am trying to create a model using the NLS function, such that:

Y = f(X) + q + e

Where f is a nonlinear (Weibull: a*(1-exp(-b*X^c)) function of X and q is a
covariate (continous variable) and e is an error term. I know that you can
create multiple nonlinear regressions where x is polynomial for example, but
is it possible to do this kind of thing when x is a function with unknown
coefficients (a,b,c)? Ultimately, I am expecting the output to give
individual regression models for each coefficient (a,b,c) with q as a
covariate.

I have tried the following code, and get the resultant error messages:
> weib.nls <- nls(Y ~ (a*(1-exp(-b*X^c)))|q,
> + data=DATA,
> + start=c(a=75,b=0.05,c=0.7))
> Error in nlsModel(formula, mf, start, wts) : 
>   singular gradient matrix at initial parameter estimates
> > summary(weib.nls)

> > weib.nls2 <- nls(Y~ (a*(1-exp(-b*X^c)))+q,
> + data=DATA,
> + start=c(a=75,b=0.05,c=0.7))
> Error in numericDeriv(form[[3L]], names(ind), env) : 
>   Missing value or an infinity produced when evaluating the model
> 

Many thanks in advance!
Lindsay

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