[R] how lambda is computed in smoot.spline given _df_

[Wolski]

Hi,

I posted some days ago a question concerning the computation of lambda in the smooth.spline function (which I repreat at the bottom of the mail) given _df_ . 
Unfortunately the documentation is not clear to me. Maybee someone can help to answer in my view the basic question: 

If the penalized log likelihood is L = (y - f)' W (y - f) + lambda c' Sigma c

how the _lambda_ in the above equation is computed if _df_ is given and _spar_ not?

And, Is there a way to define lambda directly?

Yours.
/E


Hi,

I am usign the smooth.spline function. 

I am not sure how the _df_ (degrees of freedom) parameter, 
if set, influences _lambda_ in eq:

L = (y - f)' W (y - f) + lambda c' Sigma c

Is _df_  substituting tr(Sigma), if defined, in the equation: r = tr(X' W X) / tr(Sigma) 
which is used to compute: lambda = r * 256^(3*spar - 1)?


And how _spar_ is set if not defined?

/E