plus-.objfn: Direct sum of objective functions
In dMod: Dynamic Modeling and Parameter Estimation in ODE Models

Description Usage Arguments Details Value See Also Examples

Direct sum of objective functions

1 2	## S3 method for class 'objfn' x1 + x2

`x1`	function of class `objfn`
`x2`	function of class `objfn`

The objective functions are evaluated and their results as added. Sometimes, the evaluation of an objective function depends on results that have been computed internally in a preceding objective function. Therefore, environments are forwarded and all evaluations take place in the same environment. The first objective function in a sum of functions generates a new environment.

Object of class objfn.

normL2, constraintL2, priorL2, datapointL2

  ## Generate three objective functions
  prior <- structure(rep(0, 5), names = letters[1:5])
  
  obj1 <- constraintL2(mu = prior, attr.name = "center")
  obj2 <- constraintL2(mu = prior + 1, attr.name = "right")
  obj3 <- constraintL2(mu = prior - 1, attr.name = "left")
  
  ## Evaluate first objective function on a random vector
  pouter <- prior + rnorm(length(prior))
  print(obj1(pouter))
  
  ## Split into fixed and non-fixed part
  fixed <- pouter[4:5]
  pouter <- pouter[1:3]
  print(obj1(pouter, fixed = fixed))
  
  
  ## Visualize the result by a parameter profile
  myfit <- trust(obj1, pouter, rinit = 1, rmax = 10, fixed = fixed)
  myprof <- profile(obj1, myfit$argument, "a", fixed = fixed)
  plotProfile(myprof)
  
  
  ## Create new objective function by adding the single ones,
  ## then evalue the random vector again
  pouter <- prior + rnorm(length(prior))
  obj <- obj1 + obj2 + obj3
  print(obj(pouter))