search for: objdtass

...) objdta[ , c( "y", "t" ) ] <- mydata[ objdta$rowidx , c( "y", "x" ) ] objdta$tf <- factor( objdta$t ) objdta$myfun <- with( objdta , myfun( 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...

..."t" ) ] <- mydata[ objdta$rowidx > , c( "y", "x" ) ] > objdta$tf <- factor( objdta$t ) > objdta$myfun <- with( objdta > , myfun( 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...

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.

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