Displaying 4 results from an estimated 4 matches for "objdtass2min".
2017 Jun 18
3
R_using non linear regression with constraints
...ggregate( ( objdta$myfun2 - objdta$y )^2
, objdta[ , c( "a", "b" ) ]
, FUN = function( x )
if ( all( is.na( x ) ) ) NA
else sum( x, na.rm=TRUE )
)
objdtass2min <- objdtass2[ which.min( objdtass2$x ), ]
myfit2 <- nlsLM( y ~ myfun2( a, log1ab, r = 2, t = x )
, data = mydata
, start = list( a = 2000
, log1ab = 4.60517
)
, lower = c( 1000, 0 )...
2017 Jun 18
0
R_using non linear regression with constraints
...jdta$y )^2
> , objdta[ , c( "a", "b" ) ]
> , FUN = function( x )
> if ( all( is.na( x ) ) ) NA
> else sum( x, na.rm=TRUE )
> )
> objdtass2min <- objdtass2[ which.min( objdtass2$x ), ]
>
> myfit2 <- nlsLM( y ~ myfun2( a, log1ab, r = 2, t = x )
> , data = mydata
> , start = list( a = 2000
> , log1ab = 4.60517
> )
>...
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