[R] nls question continued

[gb@stat.umu.se]

Douglas,

Thank you for the prompt answer. However, I still have
difficulties, see below. 

On 14 Feb 2000, Douglas Bates wrote:

> gb at stat.umu.se writes:
> 
> > I have two functions, tids1 and tids2:
> > 
> > tids1 <- function(W, w)
> >   {
> >     nls(W ~ w %*% beta,
> >         data = parent.frame(0),
> >         start = list(beta = rep(0, ncol(w)) ) )
> > }
> >     
> > tids2 <- function(W, w)
> > {
> >   X.1 <- w[, 1]
> >   X.2 <- w[, 2]
> >   
> >   nls(W ~ X.1 * beta1 + X.2 * beta2,
> >       data = parent.frame(0),
> >       start = list(beta1 = 0, beta2 = 0))
> > }
> > 
> > I want something like tids1, but only tids2 works:
> > 
> > > tids1(Y, X)
> > Error in qr.qty(QR, resid) : qr and y must have the same number of rows
> > 
> > > tids2(Y, X)
> > Nonlinear regression model
> >   model:  W ~ X.1 * beta1 + X.2 * beta2 
> >    data:  parent.frame 0 
> >    beta1    beta2 
> > 1.200331 1.027278 
> >  residual sum-of-squares:  5.358129 
> > 
> > Q: How do I write a _general_ tids2?
> 
> The nls function has an argument "algorithm" which can be set to
> "plinear" for partially linear models.  This will fits exactly this
> kind of model, assuming that the matrix w itself depends on some
> parameters (if it doesn't you have a linear regression model and no
> need to use nls).
> 
> Look at
>  example(nls)
> to see how that argument is used.
> 
> 
>  nls> fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)), 
>      data = DNase1, start = list(xmid = 0, scal = 1), alg = "plinear", 
>      trace = TRUE)
>  0.7139315 : 0.000000 1.000000 1.453853 
>  0.1445295 : 1.640243 1.390186 2.461754 
>  0.008302151 : 1.620899 1.054228 2.478388 
>  0.004794192 : 1.485226 1.043709 2.347334 
>  0.004789569 : 1.483130 1.041468 2.345218 
>  0.004789569 : 1.483090 1.041455 2.345180 

This works alright, but what if you want to use 'data = DNase' instead,
and include DNase$Run as a factor in the model, for instance linearly?

This is close to my _real_ problem, where the model is something like

y ~ (x.1 * beta1 + x.2 * beta2)^delta + X %*% Alpha

where  X  is a matrix created from indicators of the levels of a 
factor. 'delta' is a known constant, beta1 and beta2 are scalar
parameters, and Alpha is vector of parameters.

Could this be done? 

Göran
----------------------------------------------------------
 Göran Broström                      tel: +46 90 786-5223
 Department of Statistics            fax: +46 90 786-6614
 Umeå University                                         
 SE-90187 Umeå, Sweden              email: gb at stat.umu.se
                                                        
 http://www.stat.umu.se/egna/gb    ftp://capa.stat.umu.se
----------------------------------------------------------

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