Displaying 4 results from an estimated 4 matches for "objdtass2".
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objdtass
2017 Jun 18
3
R_using non linear regression with constraints
...log1ab <- with( objdta, log( 1000 - a * b ) )
objdta$myfun2 <- with( objdta
, myfun2( a = a
, log1ab = log1ab
, r = 2
, t = t
)
)
objdtass2 <- aggregate( ( objdta$myfun2 - objdta$y )^2
, objdta[ , c( "a", "b" ) ]
, FUN = function( x )
if ( all( is.na( x ) ) ) NA
else sum( x, na.rm=TRUE )...
2017 Jun 18
0
R_using non linear regression with constraints
...* b ) )
> objdta$myfun2 <- with( objdta
> , myfun2( a = a
> , log1ab = log1ab
> , r = 2
> , t = t
> )
> )
> objdtass2 <- aggregate( ( objdta$myfun2 - objdta$y )^2
> , objdta[ , c( "a", "b" ) ]
> , FUN = function( x )
> if ( all( is.na( x ) ) ) NA
> else sum( x, na.rm=TRUE...
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