Displaying 4 results from an estimated 4 matches for "objdtassmin".
Did you mean:
objdtass2min
2017 Jun 18
3
R_using non linear regression with constraints
...( a = a, b = b, r = 2, t = t )
)
objdtass <- aggregate( ( objdta$myfun - objdta$y )^2
, objdta[ , c( "a", "b" ) ]
, FUN = function( x )
sum( x, na.rm=TRUE )
)
objdtassmin <- objdtass[ which.min( objdtass$x ), ]
myfit <- nlsLM( y ~ myfun( a, b, r=2, t=x )
, data = mydata
, start = list( a = 2000
, b = 0.05
)
, lower = c( 1000, 0 )
, upper = c(...
2017 Jun 18
0
R_using non linear regression with constraints
...)
> objdtass <- aggregate( ( objdta$myfun - objdta$y )^2
> , objdta[ , c( "a", "b" ) ]
> , FUN = function( x )
> sum( x, na.rm=TRUE )
> )
> objdtassmin <- objdtass[ which.min( objdtass$x ), ]
>
> myfit <- nlsLM( y ~ myfun( a, b, r=2, t=x )
> , data = mydata
> , start = list( a = 2000
> , b = 0.05
> )
> , lower = c( 100...
2017 Jun 18
0
R_using non linear regression with constraints
I ran the following script. I satisfied the constraint by
making a*b a single parameter, which isn't always possible.
I also ran nlxb() from nlsr package, and this gives singular
values of the Jacobian. In the unconstrained case, the svs are
pretty awful, and I wouldn't trust the results as a model, though
the minimum is probably OK. The constrained result has a much
larger sum of squares.
2017 Jun 18
3
R_using non linear regression with constraints
https://cran.r-project.org/web/views/Optimization.html
(Cran's optimization task view -- as always, you should search before posting)
In general, nonlinear optimization with nonlinear constraints is hard,
and the strategy used here (multiplying by a*b < 1000) may not work --
it introduces a discontinuity into the objective function, so
gradient based methods may in particular be