Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/convexpr2nlform.R
Convert two sided (or one sided) expression formula to nl.form
object using derive3
from MASS
library.
1 |
form |
Must be one sided expression (defined by ~formula) or two sided (response~predictor), nonlinear regression function, include parameters, response and predictor variables. |
namesdata |
optional character vector of name of data include independent and possibly dependent in two sided fomula. |
start |
list of parameters, for which the gradinet and hessian will be computed. |
name |
A character name for the model |
inv |
inverse of the nonlinear functin model |
... |
Ane extra argument pass to |
nlr
package is gradient based algorithm, is based nl.form
object in which gradient and hessian is available. If a nonlinear regression model formula is one sided or two sided formula and its gradient and hessian exist, the convexpr2nlform
convert it to nl.form
object by calling derive3
from MASS
library. Although the existence of derivative is strong assumption but using advance programs can acheive high precision computing.
nl.form
object of the nonlinear regression function.
formula: |
formula one sided or two sided with gradinet and hessian as attribute. |
formtype: |
="formula" |
p: |
=length(start) is number of parameters. |
name: |
="User Defined" |
par: |
=start parameters. |
dependent |
character vector of name of dependent variable. |
independent: |
character vector of name of independent variable. |
origin: |
=form |
If the derivatives does not exist in nlr
function eplicitly the derivative option must set to derivative free.
The namesdata
is not functional in this version, implemented for further development. The name of parameters will be constructed from start
arguments and the name of independent and dependent variables will be derived from the rest of variables embeded in the form
expression.
Hossein Riazoshams, May 2014. Email: riazihosein@gmail.com URL http://www.riazoshams.com/nlr/
Rizo ML 2008 Statistical Computing with R The R Series. Chapman & Hall/CRC The R Series.
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