Nonlinear Fitting Object
- DESCRIPTION:
-
This object is returned by the function ms() to represent
the result of fitting a nonlinear model by general minimization.
- STRUCTURE:
-
The object contains the final parameter values, corresponding function
and gradient values, and final values for the flags generated internally
in the minimization algorithm.
If the model was defined in terms of n contributions from n observations,
as in the case of minimizing the negative log-likelihood, the
function value and derivatives will also be returned on a per-observation
form for use in plots, etc.
- parameters:
-
the final values of the parameters in the estimation.
- formula:
-
the formula used for the estimation.
- call:
-
an image of the call to ms(), but with all the arguments explicitly
named, so that the data component of the call will always give the data
argument, and so on.
- value, gradient,:
-
the final objective function value (one number), and optionally the
corresponding gradient vector and
- hessian:
-
matrix of second derivatives,
if these were computed. For hessian, only the lower triangle is returned.
- pieces, slopes,:
-
these are the contributions of the N observations to, respectively,
the value, the gradients and the
- curves:
-
hessian of the objective function.
The first is a vector of length N, the other two are matrices with
N rows.
The slopes component is only returned if derivatives are computed and
the curves component only if second derivatives are computed.
- scale:
-
the scaling vector used by the optimization algorithm.
- opt.parameters,:
-
these are the floating point and integer parameters used and generated
by the underlying Fortran
- flags:
-
algorithm.
You hope you don't need to know about them, but if you do, see the
documentation for the algorithm dmnf in the Port library.
- assign:
-
a list, mapping elements of the parameters component
to the named parameters (e.g., as specified by the
start argument).
Note that the names attribute of parameters gives the individual
names; where an element of assign is of length > 1, the individual
parameter names will be extended to be unique.
The elements of this list are weakly analagous to the terms in a
linear model, and the assign components in this sense serve the
same function in both cases.
- data:
-
optionally,
a copy of the data argument with the final value of the parameters.
This is returned if the control in the fit included data=T.
The returned data will be a paremetrized data frame if the data in
the fit inherited from "data.frame".
- SEE ALSO:
-
nls.object